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Growing public interest in understanding the origins and production methods of dairy products, driven by concerns related to environmental impact, local sourcing, and ethics, highlights an important trend. Nevertheless, a knowledge-trust gap persists between consumers and the dairy industry. Addressing this gap, in this paper, we developed an immersive virtual farm simulation to provide realistic on-farm experiences to the public. Within the virtual farm, users can explore various sites where dairy cows are raised and gain insights into dairy production processes using a head-mounted display (HMD). This simulation was demonstrated at local libraries, involving 48 public participants. We collected and analyzed participants' feedback on various aspects, including usability and their overall perceptions, to assess the simulation's effectiveness as an agricultural education tool. We investigated the impact of the virtual experience on participants' perceived knowledge gain and their awareness of the dairy industry. The results indicate that our dairy farm simulation was positively received as an effective tool for public education. Emphasizing the potential of virtual reality (VR) simulations in agricultural education and the industry, we discuss our key findings and future plans. [Display omitted] • Development process to build a realistic immersive simulation of a dairy farm for public education purposes. • Findings on the usability and user perception of the immersive experience for education purposes. • The system is an effective and useful tool for learning and the provision of information about the dairy industry. [ABSTRACT FROM AUTHOR]

For many applications, remote guidance and telerobotics provide great advantages. For example, tele-ultrasound can bring much-needed expert healthcare to isolated communities. However, existing tele-guidance methods have serious limitations including either low precision for video conference-based systems, or high complexity and cost for telerobotics. A new concept called human teleoperation leverages mixed reality, haptics, and high-speed communication to provide tele-guidance that gives an expert nearly-direct remote control without requiring a robot. This paper provides an overview of the human teleoperation concept and its application to tele-ultrasound. The concept and its impact are discussed. A new approach to remote streaming and control of point-of-care ultrasound systems independent of their manufacturer is described, as is a high-speed communication system for the HoloLens 2 that is compatible with ResearchMode API sensor stream access. Details of these systems are shown in supplementary video demonstrations. Novel interaction methods enabled by HoloLens 2-based pose tracking are also introduced and tests of the communication and user interaction are presented. The results show continued improvement of the system compared to previous work in instrumentation, HCI, and communication. The system thus has good potential for tele-ultrasound, as well as possible other applications of human teleoperation including remote maintenance, inspection, and training. The remote ultrasound streaming and control application is made available open source. [Display omitted] • System improvements to human teleoperation demonstrate its feasibility. • Other devices such as the Nreal Light can be used for implementation. • New device-agnostic remote ultrasound streaming and control demonstrated. • HoloLens pose tracking enables human teleoperation with limited compute resources. • HoloLens-based communication system provides effective sensor data streaming. [ABSTRACT FROM AUTHOR]

Learning 3D mesh representation is necessary for many computer vision and graphic tasks. Recently, some works have studied convolution methods for directly processing input meshes. However, these methods are usually weak in extracting local geometry information because of the disadvantages such as isotropic filter, neglect of mesh topology, and a small convolution field. In this paper, we introduce a local geometry-perceptive mesh convolution, which pays attention to mesh irregular structures for efficiently capturing geometry features in a multi-ring receptive field. Specifically, we define each template node's dynamic neighbor-attention weights used in multiple attention aggregation operations for obtaining local mesh change information of different vertices in the multi-ring field. After each aggregation, a shared anisotropic filter maps the catenation of each new vertex and its neighbors for extracting geometry features of the current ring. Then, complete local geometry features of each vertex in its large local field are obtained by summing the mapped results of each aggregation. Moreover, the position features of each vertex are added to its local geometry features to get the final representation vector of the vertex. We demonstrate the proposed mesh convolution method's strong ability in modeling 3D mesh shapes. [Display omitted] • Utilizing local irregular mesh structures, dynamic neighbor-attention weights of template's nodes perceive local mesh change information of different vertices. • Multiple attention aggregations and shared anisotropic filters help extract geometry features of large local receptive fields with multi-ring. • The proposed method determines our deep 3D morphable models (3DMMs) have smaller sizes than previous models and achieve state-of-the-art performances. [ABSTRACT FROM AUTHOR]

Point cloud completion aims to estimate the whole shapes of objects from their partial scans, and one of the main obstacles that prevents current methods from being applied in real-world scenarios is the variety of structural losses in real-scanned objects, which can hardly be fully included and reflected by the training samples. In this paper, we introduce Patch Sequence Autoregression Network (PSA-Net), a learning-based method that can be trained without the partial point clouds in dataset and is inherently adaptable to input scans with different levels of shape incompleteness: It makes restoring the unseen parts of objects be equivalent to predicting the missing tokens in local patch embedding sequences, and such prediction can start from any initial states. Specifically, we first introduce a Sequential Patch AutoEncoder that reconstructs complete point clouds from quantized patch feature sequences. Second, we establish a Mixed Patch Autoregression pipeline that can flexibly infer the whole sequence from any number of known tokens at any positions. Third, we propose a Voting-Based Mapping module that makes input points softly vote for their possible related tokens in sequences based on their local areas, which transforms partial point clouds to masked sequences in test. Quantitative and qualitative evaluations on two synthetic and four real-world datasets illustrate the competitive performances of our network when comparing with existing approaches. [Display omitted] • A Sequential Patch AutoEncoder for shape generation from quantized feature sequence. • A Mixed Patch Autoregression pipeline for token prediction from any initial states. • A Voting-based Mapping module for transformation from partial shapes to sequences. • Competitive performances on two synthetic and four real-world datasets. [ABSTRACT FROM AUTHOR]

Low-light images often suffer from severe visual degradation, affecting both human perception and high-level computer vision tasks. Most existing methods process images in the spatial domain, making it challenging to simultaneously improve brightness while suppressing noise. In this paper, we present a novel perspective to enhance images based on frequency domain characteristics. Specifically, we reveal that the low-frequency components are closely related to luminance and color, whereas the high-frequency components are not. Based on this observation, we propose the Frequency-aware Network (FaNet) for low-light image enhancement. By selectively adjusting low-frequency components, FaNet preserves more high-frequency details while achieving low-light image enhancement. Additionally, we employ a multi-scale framework and selective fusion for effective feature learning and image reconstruction. Experimental results demonstrate the superiority of the proposed method. [Display omitted] • We reveal that the luminance is closely related to low-frequency components. • We design a frequency-aware network to utilize frequency domain features. • A multi-scale framework and selective fusion is proposed for feature learning. [ABSTRACT FROM AUTHOR]

3D meshes are commonly utilized and may be considered to be the most popular surface representation in computer graphics due to their simplicity, efficiency and flexibility. However, the explicit storage of mesh vertices and connectivity, as in widely-used PLY and OBJ file formats, leads to substantial memory consumption. This, in turn, directly affects the processing and transmission in downstream applications. Though mesh simplification and mesh compression are common strategies to lessen memory consumption, they exhibit inherent limitations either in maintaining a balance between accuracy, efficiency, memory usage and mesh quality, or breaking the simplicity of explicit storage and struggling with optimizing the trade-off between compression performance and computational resource consumption. To overcome these limitations, inspired by the Gaussian Mixture Model (GMM), this paper proposes a GMM vertex-optimization-based implicitly-connected quadrilateral format for 3D mesh storage, named GMM-ICQ. Extensive qualitative and quantitative evaluations demonstrate that the GMM-ICQ format achieves efficient compression by retaining only a small amount of vertex information, while preserving sharp features and maintaining relatively high mesh quality. It also exhibits a certain degree of robustness in the presence of noise interference. Furthermore, benefiting from the inherent grid-based connectivity, the GMM-ICQ format maintains the simplicity of explicit storage and can be implemented as a progressive variant without incurring additional computational overhead. [Display omitted] • We present a GMM vertex-optimization-based implicitly-connected quadrilateral format for 3D mesh storage. • Simultaneously balances accuracy, efficiency, memory usage, and mesh quality. • Preserves the simplicity of explicit storage (such as PLY and OBJ). • No additional computational overhead needed for progressive variant implementation. [ABSTRACT FROM AUTHOR]

Typical non-local edge-aware filtering methods build long-range connections by deriving a minimum spanning tree (MST) from the input image. Each pixel on the MST only connects to a sub-set of pixels in the 8-connected neighborhood, resulting in piece-wise constant output with fake edges among sub-trees for the unbalanced information propagation along eight directions. In this paper, we propose two complementary spatial trees to incorporate information from the entire image. The structure of each tree depends on the spatial relationships of neighboring pixels. The distances between any two pixels in both spatial space and intensity space are the shortest distances on each tree. We introduce an efficient algorithm to recursively compute the output and the normalization constant on each tree with linear time complexity. For each pixel, we first calculate the outputs from eight subtrees and then fuse them to obtain the result on each tree structure. The final filtering output of our method is the weighted average of the results from two complementary spatial trees. Moreover, we present a distance mapping scheme to adjust the intensity distance between neighboring pixels, enabling our method to filter out a manageable degree of low-amplitude structures while sharpening major edges. Extensive experiments in graphic applications, such as image denoising, JPEG artifact removal, tone mapping, detail enhancement, and colorization, demonstrate the effectiveness and versatility of our method. [Display omitted] • Novel complementary trees to estimate the geodesic distance between any two pixels. • Efficient algorithms with linear time complexity to compute the weighted average of all pixels in the input image. • A distance mapping scheme in intensity space to manageably filter out low-amplitude structures. • Quantitative evaluation and qualitative comparison on various graphic applications to show the effectiveness and the versatility of our approach. [ABSTRACT FROM AUTHOR]

Image animation creates visually compelling effects by animating still source images according to driving videos. Recent work performs animation on arbitrary objects using unsupervised methods and can relatively robustly perform motion transfer on human bodies. However, the complex representation of motion and unknown correspondence between human bodies often lead to issues such as distorted limbs and missing semantics, which make human animation challenging. In this paper, we propose a semantically guided, unsupervised method of motion transfer, which uses semantic information to model motion and appearance. Specifically, we use a pre-trained human parsing network to encode the rich and diverse foreground semantic information, thus generating fine details. Secondly, we use a cross-modal attention layer to learn the semantic region's correspondence between human bodies to guide the network in selecting appropriate input features, prompting the network to generate accurate results. Experiments demonstrate that our method outperforms state-of-the-art methods in motion-related metrics, while effectively addressing the problems of semantic missing and unclear limb structures prevalent in human motion transfer. These improvements can facilitate its applications in various fields, such as education and entertainment. [Display omitted] • Proposes a novel framework for human image animation using semantic features and cross-modal attention. • Introduces semantic segmentation features to represent motion and identity information. • Employs a cross-modal attention mechanism to establish correspondences between semantic regions. • Achieves state-of-the-art performance on complex human motions like TaiChi poses. • Reduces issues of missing semantics and distorted limbs common in human animation. [ABSTRACT FROM AUTHOR]

In recent times, there has been notable and swift advancement in the field of image dehazing. Several deep learning techniques have demonstrated remarkable proficiency in resolving homogeneous dehazing issues. Nonetheless, the current dehazing approaches are generally formulated to deal with homogeneous haze, which is often undermined in real-world scenarios due to the uncertain haze dispersion. In this paper, we propose a dehazing model named RepDehazeNet by combining a structurally Reparameterization Encoder-Decoder subnet and a Full-Resolution Attention subnet. To be specific, the structural reparameterization idea is introduced into the encoder–decoder subnet to strengthen the feature extraction of dehazed images and improve the feature extraction speed. RepDehazeNet is compared with seven SOTA models on different datasets in terms of PSNR, SSIM, parameter quantity, and inference time. Compared to the DW-GAN model, the proposed RepDehazeNet model reduces the number of parameters by 2.7 million, and improves the inference speed by 90.3%, while achieving a higher PSNR of 0.5 dB on the NH-Haze2021 dataset. The experimental results demonstrate that the proposed RepDehazeNet model can effectively improve the real-time performance, accuracy of dehazing synthesized and nonhomogeneous haze images. [Display omitted] • Structural reparameterization dehazenet: outstanding performance, faster speed. • Replacing Tanh with ReLU leads to better results. • Transfer learning addresses the problem of insufficient samples. • Dual subnets method proves highly effective in datasets of different scales. [ABSTRACT FROM AUTHOR]

Currently, deep learning-based 3D face reconstruction methods have shown promising results. However, they ignore the contextual information of the face, which is a topologically unified entirety. This paper proposes a 3D face reconstruction approach based on hybrid-level contextual information. Firstly, we suggest a regression network with contextual modeling capability at the feature level, PPR-CNet, which adopts a preferential parameter regression to regress the 3DMM parameters dynamically based on their various impacts on the reconstructed 3D face. Furthermore, we design a contextual landmark loss to constrain the face geometry at the landmark level. We introduce a differentiable renderer combined with multiple loss functions for weakly-supervised training. Quantitative experiments on two benchmarks show our method outperforms several SOTA methods. Extensive qualitative experiments indicate that our method performs efficiently in realism, facial proportion, and occlusion. [Display omitted] • We propose an approach to reconstruct a 3D face from a single image based on hybrid-level contextual information. • We propose a regression network, PPR-CNet, with contextual modeling capability, which regresses 3DMM parameters dynamically. • We design a contextual landmark loss to constrain face geometry employing landmark contextual information. [ABSTRACT FROM AUTHOR]

This paper proposes a novel curve called the α - curve , which is specified by a variable shape parameter α and can be applied in curve reconstruction involving curve fairing, fitting, and segmentation. In contrast to classical log-aesthetic curves in which α is constant, this study contributes a novel formulation of α that varies monotonically within a specified segment. By introducing the variable α , the logarithmic curvature graph (LCG) becomes piecewise linear and parabolic, or any other function if necessary. Consequently, in comparison to conventional log-aesthetic curves that have limited field usage based on their rigidity, α -curves have various LCGs and flexible representations, thereby opening up many practical applications. Experimental results and comparative studies on curves created by CAD experts in the automotive industry demonstrate the theoretical and practical validity of α -curves. [Display omitted] • α -Curves are proposed by extending the mathematical framework of log-aesthetic curves • An α -curve is specified by a variable shape parameter • α -Curves have various log-curvature graphs (LCG) and flexible representations • α -Curves are applied to a new fairing, avoiding conventional issues of side effects [ABSTRACT FROM AUTHOR]

The Special Section contains extended and revised versions of the best papers presented at the 10th Conference on Smart Tools and Applications in Graphics (STAG 2023), held in Matera on November 16–17, 2023. Four papers were selected by appointed members from the Program Committee; extended versions were submitted and further reviewed by external experts. The result is a rich collection of papers spanning diverse domains: from shape analysis and computational geometry to advanced applications in machine learning, virtual interaction, and digital fabrication. Topics include shape modeling, functional maps, and point clouds, highlighting cutting-edge research in user experience and interaction design. [Display omitted] • 10th Int. Conference on Smart Tools and Applications in Graphics (STAG 2023). • STAG 2023 received 22 submissions, 14 of which were accepted as full papers and 3 as short papers. • Extended versions of 4 selected papers, further reviewed by externals. • Shape analysis, computational geometry, machine learning, virtual interaction. • Digital fabrication, user experience, and computational design. [ABSTRACT FROM AUTHOR]

Over the past decade, there has been a significant increase in the development of visual analytics systems dedicated to addressing urban issues. These systems distill intricate urban analysis workflows into intuitive, interactive visual representations and interfaces, enabling users to explore, understand, and derive insights from large and complex data, including street-level imagery, street networks, and building geometries. Developing urban visual analytics systems, however, is a challenging endeavor that requires considerable programming expertise and interaction between various multidisciplinary stakeholders. This situation often leads to monolithic and isolated prototypes that are hard to reproduce, combine, or extend. Concurrently, there has been an increase in the availability of general and urban-specific toolkits, frameworks, and authoring tools that are open source and abstract away the need to implement low-level visual analytics functionalities. This paper provides a hierarchical taxonomy of urban visual analytics systems to contextualize how they are usually designed, implemented, and evaluated. We develop this taxonomy across three distinct levels (i.e. , dimensions, categories, and tags), juxtaposing visualization with analytics, data, and system dimensions. We then assess the extent to which current open-source toolkits, frameworks, and authoring tools can effectively support the development of components tailored to urban visual analytics, identifying their strengths and limitations in addressing the unique challenges posed by urban data. In doing so, we offer a roadmap that can guide the effective employment of existing resources and chart a pathway for developing and refining future systems. [Display omitted] • Review of over 135 papers proposing urban visual analytics systems. • Hierarchical taxonomy of urban visual analytics systems considering over 160 tags. • Evaluation of toolkits, frameworks, and authoring tools for urban visual analytics. • We highlight the need for interoperability and sustainable cyberinfrastructures. [ABSTRACT FROM AUTHOR]

Gesture recognition is a tool to enable novel interactions with different techniques and applications, like Mixed Reality and Virtual Reality environments. With all the recent advancements in gesture recognition from skeletal data, it is still unclear how well state-of-the-art techniques perform in a scenario using precise motions with two hands. This paper presents the results of the SHREC 2024 contest organized to evaluate methods for their recognition of highly similar hand motions using the skeletal spatial coordinate data of both hands. The task is the recognition of 7 motion classes given their spatial coordinates in a frame-by-frame motion. The skeletal data has been captured using a Vicon system and pre-processed into a coordinate system using Blender and Vicon Shogun Post. We created a small, novel dataset with a high variety of durations in frames. This paper shows the results of the contest, showing the techniques created by the 5 research groups on this challenging task and comparing them to our baseline method. [Display omitted] [ABSTRACT FROM AUTHOR]

• Foreword to the 16th Eurographics Symposium on 3D Object Retrieval (3DOR2023). • Research into methods and evaluation techniques for shape retrieval are presented. • It contains 9 full technical papers and 3 SHREC full papers. [ABSTRACT FROM AUTHOR]

Motion matching has become a widely adopted technique for generating high-quality interactive animation systems in video games. However, its current implementations suffer from significant computational and memory resource overheads, limiting its scalability in the context of modern video game performance profiles. Our method significantly reduces the computational complexity of approaches to motion synthesis, such as "Learned Motion Matching", while simultaneously improving the compactness of the data that can be stored and the robustness of pose output. As a result, our method enables the efficient execution of motion matching that significantly outperforms other implementations, by 8.5× times in CPU execution cost and at 80% of the memory requirements of "Learned Motion Matching", on contemporary video game hardware, thereby enhancing its practical applicability and scalability in the gaming industry and unlocking the ability to apply on large numbers of animated in-game characters. In this paper, we expand upon our published paper "Learning Robust and Scalable Motion with Lipschitz Continuity and Sparse Mixture of Experts", where we successfully proposed a novel method for learning motion matching that combines a Sparse Mixture of Experts model architecture and a Lipschitz-continuous latent space for representation of poses. We present further details on our method, with extensions to our approach to expert utilization within our neural networks. [Display omitted] • Applies the Sparse Mixture of Experts architecture to animation synthesis for the first time. • Significantly reduce theoretical and practical compute requirements, 8.5 times that of prior art. • Lipschitz-continuous encoding neural networks producing a linearly interpolatable manifold. • SMoE Networks express a higher density of animation data than Feed-Forward Neural Networks. [ABSTRACT FROM AUTHOR]

Low-light image enhancement is crucial in applications such as traffic safety and medical imaging. Besides having characteristics like low luminance and poor visibility, low-light images are inevitably affected by noise. Noise not only covers image details, introduces artifacts, and decreases image quality, but also interferes with downstream computer vision tasks like object detection, image segmentation, and object tracking. Previous methods in image enhancement often overlook noise handling or fail to accurately suppress noise in adaptive denoising processes, resulting in more severe noise artifacts in the enhanced images. To effectively suppress noise, this paper proposes a Channel Self-Attention Based Low-Light Image Enhancement Network (CAENet), which leverages Transformers and CNNs to model long-range and short-range pixel dependencies, extract global and local features, and construct a Noise Suppression Transformer Block that adaptively suppresses noise regions guided by signal-to-noise ratio priors and attention maps. After adaptive noise suppression, the resulting images exhibit fewer noise artifacts and improved details. The experimental results show that the network in this paper outperforms other state-of-the-art methods overall on five representative paired datasets as well as six unpaired datasets, improving the image quality while effectively suppressing the noise. [Display omitted] • To address the problem of diverse noise types and complex interference levels in low-light image enhancement, we propose an end-to-end network CAENet. • Since the severity of noise varies in regions with different luminance, we designed the NSTB based on channel self-attention. • Aiming at the different degree of interference of noise to the whole and details of the image in different sized regions, we design global and local branches. [ABSTRACT FROM AUTHOR]

In this paper, we present a new volumetric ambient occlusion algorithm called Contextual Ambient Occlusion (CAO) that supports real-time clipping. The algorithm produces ambient occlusion images of exactly the same quality as Local Ambient Occlusion (LAO) while enabling real-time modification to the shape used to clip the volume. The main idea of the algorithm is that clipping only affects the ambient value of a small number of voxels, so by identifying these voxels and recalculating the ambient factor only for them, it is possible to significantly increase the rendering performance (by 2-5x) without decreasing the quality of the rendered image. Due to its fast performance, the algorithm is suitable for interactive environments where clipping changes could occur every frame. Additionally, the algorithm does not have any stereoscopic inconsistency, which makes it suitable for mixed reality environments. This paper is an extended version of the "Contextual Ambient Occlusion" article presented during the 2023 Graphics Interface conference, and includes, among other additions, the source code of the algorithm. [Display omitted] • Clipping only affects the ambient value of a small number of voxels. • It is more efficient to perform recalculation only on voxels affected by clipping. • Contextual Ambient Occlusion achieves same quality as Local Ambient Occlusion. • Contextual Ambient Occlusion is suitable for interactive environments. [ABSTRACT FROM AUTHOR]

• Articles published in the special section SMI2024 are referenced. • Statistics on submission and acceptance at SMI2024 are given. • Awardees of SMI2024 prizes are listed. • Best paper award and Honorable mention of SMI2024 are cited. [ABSTRACT FROM AUTHOR]

Convolutional Neural Networks (CNNs) have proven highly effective for demosaicking, transforming raw Color Filter Array (CFA) sensor samples into standard RGB images. Directly applying convolution to the CFA tensor can lead to misinterpretation of the color context, so existing demosaicking networks typically embed the CFA tensor into the Euclidean space before convolution. The most prevalent embedding options are Reordering and Pre-interpolation. However, it remains unclear which option is more advantageous for demosaicking. Moreover, no existing demosaicking network is suitable for conducting a fair comparison. As a result, in practice, the selection of these two embedding options is often based on intuition and heuristic approaches. This paper addresses the non-comparability between the two options and investigates whether pre-interpolation contributes additional knowledge to the demosaicking network. Based on rigorous mathematical derivation, we design pairs of end-to-end fully convolutional evaluation networks, ensuring that the performance difference between each pair of networks can be solely attributed to their differing CFA embedding strategies. Under strictly fair comparison conditions, we measure the performance contrast between the two embedding options across various scenarios. Our comprehensive evaluation reveals that the prior knowledge introduced by pre-interpolation benefits lightweight models. Additionally, pre-interpolation enhances the robustness to imaging artifacts for larger models. Our findings offer practical guidelines for designing imaging software or Image Signal Processors (ISPs) for RGB cameras. [Display omitted] • Solving the non-comparability between the pre-interpolation and reordering embedding schemes of demosaicking neural networks. • Conducting an objectively comparative study on the two embedding options in various situations. • Providing practical guidelines to imaging software designers on how to choose the embedding options subject to hardware conditions. [ABSTRACT FROM AUTHOR]

3D face animation has been a critical component of character animation in a wide range of media since the early 90's. The conventional process for animating a 3D face is usually keyframe-based, which is labor-intensive. Therefore, the film and game industries have started using live-action actors' performances to animate the faces of 3D characters, the process is also known as performance-driven facial animation. At the core of performance-driven facial animation is facial motion retargeting, which transfers the source facial motions to a target 3D face. However, facial motion retargeting still has many limitations that influence its capability to further assist the facial animation process. Existing motion retargeting frameworks cannot accurately transfer the source motion's semantic information (i.e., meaning and intensity of the motion), especially when applying the motion to non-human-like or stylized target characters. The retargeting quality relies on the parameterization of the target face, which is time-consuming to build and usually not generalizable across proportionally different faces. In this survey paper, we review the literature relating to 3D facial motion retargeting methods and the relevant topics within this area. We provide a systematic understanding of the essential modules of the retargeting pipeline, a taxonomy of the available approaches under these modules, and a thorough analysis of their advantages and limitations with research directions that could potentially contribute to this area. We also contributed a 3D character categorization matrix, which has been used in this survey and might be useful for future research to evaluate the character compatibility of their retargeting or face parameterization methods. [Display omitted] • A review of 3D facial motion retargeting. • Provides a comprehensive taxonomy of retargeting methods and other relevant topics. • Topics include 3D face parameterization, motion analysis, and retargeting methods. • Also relates to facial animation, motion capture, and visual effects. [ABSTRACT FROM AUTHOR]

This research paper explores the impact of augmented reality (AR) tracking characteristics, specifically an AR head-worn display's tracking registration accuracy and precision, on users' spatial abilities and subjective perceptions of trust in and reliance on the technology. Our study aims to clarify the relationships between user performance and the different behaviors users may employ based on varying degrees of trust in and reliance on AR. Our controlled experimental setup used a 360° field-of-regard search-and-selection task and combines the immersive aspects of a CAVE-like environment with AR overlays viewed with a head-worn display. We investigated three levels of simulated AR tracking errors in terms of both accuracy and precision (+0°, +1°, +2°). We controlled for four user task behaviors that correspond to different levels of trust in and reliance on an AR system: AR-Only (only relying on AR), AR-First (prioritizing AR over real world), Real-Only (only relying on real world), and Real-First (prioritizing real world over AR). By controlling for these behaviors, our results showed that even small amounts of AR tracking errors had noticeable effects on users' task performance, especially if they relied completely on the AR cues (AR-Only). Our results link AR tracking characteristics with user behavior, highlighting the importance of understanding these elements to improve AR technology and user satisfaction. [Display omitted] • Augmented Reality (AR) tracking factors include accuracy and precision. • Search-and-selection task tested effects of tracking factors on performance, trust. • Results indicate even small AR tracking errors impact user performance, trust. • Negative performance effects can be mitigated if users do not rely solely on AR. [ABSTRACT FROM AUTHOR]

Virtual Humans (VHs) emerged over 50 years ago and have since experienced notable advancements. Initially, developing and animating VHs posed significant challenges. However, modern technology, both commercially available and freely accessible, has democratized the creation and animation processes, making them more accessible to users, programmers, and designers. These advancements have led to the replication of authentic traits and behaviors of real actors in VHs, resulting in visually convincing and behaviorally lifelike characters. As a consequence, many research areas arise as functional VH technologies. This paper explored the evolution of four areas and emerging trends related to VHs while examining some of the implications and challenges posed by highly realistic characters within these domains. [Display omitted] • We present a detailed foundation and history of Virtual Humans. • We conducted a literature review on four relevant areas for Virtual Humans • Areas are: Perception; Facial Transferring; Conversational Agents; and Crowd Simulation. • We discuss the main challenges still present in the four mentioned areas. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the suitability of different visual representations of pathological growth and shrinkage using surface models of intracranial aneurysms and liver tumors. By presenting complex medical information in a visually accessible manner, audiences can better understand and comprehend the progression of pathological structures. Previous work in medical visualization provides an extensive design space for visualizing medical image data. However, determining which visualization techniques are appropriate for a general audience has not been thoroughly investigated. We conducted a user study (n = 40) to evaluate different visual representations in terms of their suitability for solving tasks and their aesthetics. We created surface models representing the evolution of pathological structures over multiple discrete time steps and visualized them using illumination-based and illustrative techniques. Our results indicate that users' aesthetic preferences largely coincide with their preferred visualization technique for task-solving purposes. In general, the illumination-based technique has been preferred to the illustrative technique, but the latter offers great potential for increasing the accessibility of visualizations to users with color vision deficiencies. [Display omitted] • We identify key aspects for the communication of pathological growth and shrinkage. • We evaluated the visualizations in a between-subject study with 40 participants. • We describe the results regarding interaction aesthetics and comprehensibility. • Insights into the convergence and divergence of visual preferences of non-experts. • We discuss reusable concepts for future work in narrative medical visualization. [ABSTRACT FROM AUTHOR]

The Shape Modeling International awards (SMI awards) were introduced to commemorate the passing of SMI founder, Professor Kunii. Since 2021, the SMI awards recognize exceptional contributors to Shape Modeling. Currently, there are three awards: the Tosiyasu Kunii Distinguished Researcher, the Young Investigator, and the Alexander Pasko Service Award. The 2024 Distinguished Researcher awardees are Gershon Elber and Stefanie Hahmann. The 2024 Young Investigators are Gianmarco Cherchi and Amal Dev Parakkat. The 2024 Service Awardee is Ergun Akleman. This article provides interviews with the five SMI 2024 award winners. [Display omitted] • We present interviews with Shape Modeling International (SMI) 2024 award winners. • We provided an overview of history of SMI conference. • For each award winner, we provided a bio and interview. • We also cited their papers published in proceedings of Shape Modeling International and Computers & Graphics to demonstrate their contributions to SMI community. • We provided also rare pictures from SMI conferences. [ABSTRACT FROM AUTHOR]

Analysis of 3D textures, also known as relief patterns is a challenging task that requires separating repetitive surface patterns from the underlying global geometry. Existing works classify entire surfaces based on one or a few patterns by extracting ad-hoc statistical properties. Unfortunately, these methods are not suitable for objects with multiple geometric textures and perform poorly on more complex shapes. In this paper, we propose a neural network for binary segmentation to infer per-point labels based on the presence of surface relief patterns. We evaluated the proposed architecture on a high resolution point cloud dataset, surpassing the state-of-the-art, while maintaining memory and computation efficiency. [Display omitted] • A deep learning model for geometric texture segmentation on 3D surfaces. • Architecture design based on distinctive features of relief patterns. • Application of geodesic Voronoi diagrams to reduce memory usage during training. [ABSTRACT FROM AUTHOR]

Recent research in arbitrary style transfer has highlighted challenges in maintaining the balance between content structure and style patterns. Moreover, the improper application of style patterns onto the content image often results in suboptimal quality. In this paper, a novel style transfer network, called MCNet, is proposed. It is based on multi-feature correlations. To better explore the intrinsic relationship between the style image and the content image and to transfer the most suitable style onto the content image, a novel Global Style-Attentional Transfer Module, named GSATM, is introduced in this work. GSATM comprises two parts: Forward Adaptive Style Transformation (FAST) and Delayed Style Transformation (DST). The former analyzes the relationship between style and content features and fine-tunes the style features, whereas the latter transfers the content features based on the fine-tuned style features. Moreover, a new encoding and decoding structure is designed to effectively handle the output of GSATM. Extensive quantitative and qualitative experiments fully demonstrate the superiority of our algorithm. Project page: https://github.com/XiangJinCherry/MCNet. [Display omitted] • We propose a new algorithm that can perform arbitrary style transfer. • We propose a novel Global Style-Attentional Transfer Module (GSATM). • We design an Adaptive Unified Decoder to fuse the outputs of the multiple GSATMs. [ABSTRACT FROM AUTHOR]

Deep neural networks have consistently represented the state of the art in most computer vision problems. In these scenarios, larger and more complex models have demonstrated superior performance to smaller architectures, especially when trained with plenty of representative data. With the recent adoption of Vision Transformer (ViT) based architectures and advanced Convolutional Neural Networks (CNNs), the total number of parameters of leading backbone architectures increased from 62M parameters in 2012 with AlexNet to 7B parameters in 2024 with AIM-7B. Consequently, deploying such deep architectures faces challenges in environments with processing and runtime constraints, particularly in embedded systems. This paper covers the main model compression techniques applied for computer vision tasks, enabling modern models to be used in embedded systems. We present the characteristics of compression subareas, compare different approaches, and discuss how to choose the best technique and expected variations when analyzing it on various embedded devices. We also share codes to assist researchers and new practitioners in overcoming initial implementation challenges for each subarea and present trends for Model Compression. • Survey on model compression techniques for computer vision applications. • Evaluation of model compression subareas in terms of research interest. • Presentation of model compression subareas with pseudocode and GitHub coding examples. [ABSTRACT FROM AUTHOR]

Image segmentation is one of the most classical computer vision tasks. Segmentation tasks yield a set of classes attributed to individual pixels instead of sparsely predicted images or patches, such as in classification or detection tasks. However, creating annotation sets for pixelwise tasks is a very costly task, often requiring hours for labeling single samples in images with multiple classes of objects. In this context, unsupervised learning can be leveraged either to expedite the annotation procedure and/or to guide the segmentation algorithms altogether without the need for manual annotations. Classical unsupervised segmentation methods leveraged techniques from areas as graph theory, image processing, clustering or supervised classifiers in order to achieve "shallow" pixelwise classification. These techniques usually aim to achieve superpixel over-segmentations by grouping similar pixels that should pertain to the same object. Modern deep unsupervised approaches for image segmentation aimed to group pixels in a data-driven way by using the capabilities of deep architectures to process unstructured data such as images. Later, self-supervised learning bypassed the need for labels via pretext tasks, compelling deep architectures to learn more generic features capable of enhancing downstream tasks, including segmentation. The generalized representations produced by unsupervised models have propelled the recent progress in self-supervised, few- and zero-shot learning and even general-purpose foundational models in computer vision, yielding state-of-the-art results across diverse tasks and datasets. This paper provides an overview of unsupervised and generalizable approaches for image segmentation, introduces key concepts and terminology, and discusses the main aspects of state-of-the-art methods. Additionally, we highlight prominent applications in various domains such as remote sensing, medical imaging, and geology. Finally, we discuss trends and future directions for state-of-the-art unsupervised image segmentation. [Display omitted] [ABSTRACT FROM AUTHOR]

Given an input query, a search and retrieval system fetches relevant information from a dataset. In the Engineering domain, such a system is beneficial for tasks such as design reuse. A two-dimensional (2D) sketch is more conducive for an end user to give as a query than a three-dimensional (3D) object. Such query sketches, nevertheless, will inevitably contain defects like incomplete lines, mesh lines, overdrawn areas, missing areas, etc. Since a retrieval system's results are only as good as the query, it is necessary to improve the query sketches. In this paper, the problem of transforming a defective CAD sketch into a defect-free sketch is addressed using Generative Adversarial Networks (GANs), which, to the best of our knowledge, has not been investigated before. We first create a dataset of 534 hand-drawn sketches by tracing the boundaries of images of CAD models. We then pair the corrected sketches with their corresponding defective sketches and use them for training a C-WGAN (Conditional Wasserstein Generative Adversarial Network), called SketchCleanGAN. We model the transformation from defective to defect-free sketch as a factorization of the defective input sketch and then translate it to the space of defect-free sketch. We propose a three-branch strategy to this problem. Ablation studies and comparisons with other state-of-the-art techniques demonstrate the efficacy of the proposed technique. Additionally, we also contribute to a dataset of around 58000 improved sketches using the proposed framework. [Display omitted] • A dataset of defect-free 534 hand-drawn sketches of the MCB dataset generated by tracing object boundaries. • A novel three-branch network architecture based on Conditional-Wasserstein-GAN has been proposed. • A factorization-based approach to model the mapping process of the network has been proposed. • A dataset of around 58000 improved sketches of the CADSketchNet dataset was also generated. [ABSTRACT FROM AUTHOR]

The 3D packing problem has a wide range of applications. However, the complex geometry of irregular objects leads to a sharp increase in the number of placement combinations, making it a challenging problem. In this paper, we propose a packing pipeline based on rigid body dynamics simulation to deal with two types of 3D packing problems. One is the variant bin packing problem, which involves placing more objects into a container of given dimensions to maximize space utilization. The other is the open dimension problem, where the goal is to minimize the container that can accommodate all objects. We first use heuristic placement strategies and a fast collision detection algorithm to efficiently obtain initial packing results. Then, we simulate the shaking of the container according to the dynamic principle. Combined with the vacant space filling operation, shaking the container drives the movement of objects in the container to make the arrangement of objects more compact. For the open dimension packing, the container height is optimized by adjusting the constraints of simulation in the basic pipeline. Experimental results show that our method has advantages over existing methods in both speed and packing density. [Display omitted] • Flexible framework for various packing problems, using physical simulation. • FFT-based collision metric and DBLF strategy for efficient packing initialization. • Effective optimization based on physical simulation, including shaking and filling. [ABSTRACT FROM AUTHOR]

Three-dimensional object detection plays a pivotal role in scene understanding and holds significant importance in various indoor perception applications. Traditional methods based on Hough voting are susceptible to interference from background points or neighboring objects when casting votes for the target's center from each seed point. Moreover, fixed-size set abstraction modules may result in the loss of structural information for large objects. To address these challenges, this paper proposes a three-dimensional object detection model based on seed point offset attention. The objective of this model is to enhance the model's resilience to voting noise interference and alleviate feature loss for large-scale objects. Specifically, a seed point offset tensor is first defined, and then the offset tensor self-attention network is employed to learn the weights between votes, thereby establishing a correlation between the voting semantic features and the object structural information. Furthermore, an object surface perception module is introduced, which incorporates detailed features of local object surfaces into global feature representations through vote backtracking and surface mapping. Experimental results indicate that the model achieved excellent performance on the ScanNet-V2 (mAP@0.5, 60.3%) and SUN RGB-D (mAP@0.5, 64.0%) datasets, respectively improving by 2.6% (mAP@0.5) and 5.4% (mAP@0.5) compared to VoteNet. [Display omitted] • Proposal of a novel 3D object detection model based on seed point offset attention, which achieves outstanding results on the SUN RGB-D dataset and ScanNet-V2 dataset. • An offset tensor attention network is introduced to address voting noise in VoteNet and feature loss for large objects. • The design of a surface-aware module that traces the voting process and constructs a local representation of the object's surface texture, enabling the perception of fine geometric features of the objects. [ABSTRACT FROM AUTHOR]

Foundation models, such as ULIP-2 (Xue et al., 2023) recently projected forward the field of 3D deep learning. These models are trained with significantly more data and show superior representation learning capacity in many downstream tasks like 3D shape classification and few-shot part segmentation. A particular characteristic of the recent 3D foundation models is that they are typically multi-modal , and involve image (2D) as well as caption (text) branches. This leads to an intricate interplay that benefits all modalities. At the same time, the nature of the 3D encoders alone, involved in these foundation models is not well-understood. Specifically, there is little analysis on the utility of both pre-trained 3D features provided by these models, or their capacity to adapt to new downstream 3D data. Furthermore, existing studies typically focus on label-oriented downstream tasks, such as shape classification, and ignore other critical applications, such as 3D content-based object retrieval. In this paper, we fill this gap and show, for the first time, how 3D foundation models can be leveraged for strong 3D-to-3D retrieval performance on seven different datasets, on par with state-of-the-art view-based architectures. We evaluate both the pre-trained foundation models, as well as their fine-tuned versions using downstream data. We compare supervised fine-tuning using classification labels against two self-supervised label-free fine-tuning methods. Importantly, we introduce and describe a methodology for fine-tuning, as we found this to be crucial to make transfer learning from 3D foundation models work in a stable manner. [Display omitted] • Self-supervised 3D object retrieval is competitive with supervised learning on the same data distribution. • Self-supervised 3D object retrieval outperforms supervised learning out-of-distribution. • Object alignment is highly impactful on the out-of-distribution performance of retrieval systems. • Non-contrastive siamese networks are a practical option in 3D deep learning. [ABSTRACT FROM AUTHOR]

The automatic shape description of solids is a problem of interest in manufacturing engineering, amongst other related areas. This description can be either geometrical or topological in nature and can be applied to either surfaces or solids (embedded manifolds). Topological descriptions are specially interesting for the problem of shape comparison and retrieval, where one wants to know if a given shape resembles some other known shape. Some popular topological descriptions use Morse theory to study the topology of manifolds and encode their shape characteristics. A Morse function f is defined on the manifold and the manifold's shape is indirectly studied by studying the behavior of the critical points of f. This family of methods is well defined for surfaces but does not consider the case of solids. In this paper we address the topological description of solids using Morse theory. Our methodology considers three cases: solids without internal boundaries, solids with internal boundaries and thin-walled solids. We present an algorithm to identify topological changes on these solids using the principle of shape decomposition by Morse handles. The presented algorithm deals with Morse functions that produce parallel planar level sets. Future endeavors should consider other candidate functions. [Display omitted] • We show how the Reeb graph cannot describe the shape of general solids • We present a methodology to address the shape description of general solids • We automatically identify the critical points of a solid given its B-Rep. • We apply our methodology to several datasets for Additive Manufacturing [ABSTRACT FROM AUTHOR]

Positive airway pressure (PAP) therapy refers to sleep disordered breathing treatment that uses a stream of compressed air to support the airway during sleep. Even though the use of PAP therapy has been shown to be effective in improving the symptoms and quality of life, many patients are intolerant of the treatment due to poor mask fit. In this paper, our goal is to develop a computational approach for designing custom-fit PAP masks such that they can achieve better mask fit performance in terms of mask leakage and comfort. Our key observation is that a custom-fit PAP mask should fit a patient's face in its deformed state instead of in its rest state since the PAP mask cushion undergoes notable deformation before reaching an equilibrium state during PAP therapy. To this end, we compute the equilibrium state of a mask cushion using the finite element method, and quantitatively measure the leakage and comfort of the mask cushion in this state. We further optimize the mask cushion geometry to minimize the two measures while ensuring that the cushion can be easily fabricated with molding. We demonstrate the effectiveness of our computational approach on a variety of face models and different types of PAP masks. Experimental results on real subjects show that our designed custom-fit PAP masks are able to achieve better mask fit performance than a generic PAP mask and custom-fit PAP masks designed by a state-of-the-art approach. [Display omitted] • Simulating the virtual mask-face fitting with FEM. • Proposing quantitative measures on air leakage and comfort for the mask. • Optimizing the geometry of the mask cushion to minimize our measures on leakage and comfort. • Ensuring that the cushion can be easily fabricated by molding with silicone material. [ABSTRACT FROM AUTHOR]

Text-conditioned image synthesis methods such as DALLE-2, IMAGEN, and Stable Diffusion are gaining strong attention from deep learning and art communities recently. Meanwhile, Image-to-Image (Img2Img) synthesis applications that emerged from the pioneering Neural Style Transfer (NST) approach have swiftly transitioned towards the feed-forward Automatic Style Transfer (AST) methods, due to numerous constraints inherent in the former method, including inconsistent synthesis outcomes and sluggish optimization-based synthesis process. However, NST holds significant potential yet remains relatively underexplored within this research domain. In this paper, we revisited the original NST method and uncovered its potential to attain image quality comparable to the AST synthesis methods across a diverse range of artistic styles. We propose a two-stage Feature-guided Style Transfer (FeaST) which consists (a) pre-stylization step called Sketching to address the poor initialization issue, and (b) Finetuning to guide the synthesis process based on high-frequency (HF) and low-frequency (LF) guidance channels. By addressing the issues of inconsistent synthesis and slow convergence inherent in the original method, FeaST unlocks the full capabilities of NST and significantly enhances its efficiency. [Display omitted] • We revisit the Neural Style Transfer (NST) framework for high-fidelity art generation. • We proposed a feature-guided reconstruction based on separated guidance channels for high and low-frequency feature regions to solve slow convergence issues. • We proposed a pre-stylization step called Sketching which improved the image synthesis time by a six-order magnitude. • We experimented a set of optimal weighting factors in feature-guided reconstruction for consistent results over wide variety of styles and contents. [ABSTRACT FROM AUTHOR]

In order to facilitate the robust and precise 3D vessel shape extraction and quantification from in-vivo Magnetic Resonance Imaging (MRI), this paper presents a novel multi-scale Knowledge Transfer Vision Transformer (i.e., KT-ViT) for 3D vessel shape segmentation. First, it uniquely integrates convolutional embeddings with transformer in a U-net architecture, which simultaneously responds to local receptive fields with convolution layers and global contexts with transformer encoders in a multi-scale fashion. Therefore, it intrinsically enriches local vessel feature and simultaneously promotes global connectivity and continuity for a more accurate and reliable vessel shape segmentation. Furthermore, to enable using relatively low-resolution (LR) images to segment fine scale vessel shapes, a novel knowledge transfer network is designed to explore the inter-dependencies of data and automatically transfer the knowledge gained from high-resolution (HR) data to the low-resolution handling network at multiple levels, including the multi-scale feature levels and the decision level, through an integration of multi-level loss functions. The modeling capability of fine-scale vessel shape data distribution, possessed by the HR image transformer network, can be transferred to the LR image transformer to enhance its knowledge for fine vessel shape segmentation. Extensive experimental results on public image datasets have demonstrated that our method outperforms all other state-of-the-art deep learning methods. • A novel knowledge transfer transformer for segmenting fine-scale 3D vasculature. • A unique vision transformer integrating both local convolutions and global encodings. • A U-shape multi-scale processing through the hierarchical convolutional transformer. • Knowledge transfer through transformers to explore the resolution inter-dependencies. [ABSTRACT FROM AUTHOR]

This paper addresses the design of surface shells as assemblies of tileable, flat geometric patterns with predictable performance in response to mechanical stimuli. We design a family of tileable and fabricable patterns represented as triangle meshes, which can be assembled for creating surface tessellations. First, a regular recursive subdivision of the planar space generates different geometric configurations for candidate patterns, having interesting and varied aesthetic properties. Then, a refinement step addresses manufacturability by solving for non-manifold configurations and sharp angles that would produce disconnected or weak patterns. We devise a strategy for creating continuous variations on the geometry of individual patterns, in both aesthetics and behavior, to enrich the catalog of available designs. Finally, we simulate our patterns to evaluate their mechanical response when loaded in different scenarios targeting out-of-plane bending. Through a simple browsing interface, we show that our patterns span a variety of different bending behaviors. The result is a catalog of patterns with varied aesthetics and predefined mechanical behavior, to use for the direct design of mechanical metamaterials. To assess the feasibility of our design-to-fabricate approach, we show fabricated 3D objects with different curvatures, and compare physical and simulated experiments. [Display omitted] • Tileable geometric patterns, generated through recursive subdivision and refinement for manufacturability. • The patterns exhibit diverse mechanical behaviors computed in standard out-of-plane bending scenarios. • Strategy for domain augmentation by variating continuous parameters. • Navigation of a design catalog to select desirable aesthetic and mechanical qualities. • Design-to-fabricate approach validated through digital fabrication of 3D objects in comparison with simulations. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel pipeline to compute and refine a data-driven solution for estimating the correspondence between 3D point clouds. Our method is compatible with the functional map framework, so it relies on a functional representation of the correspondence, but, differently from other similar approaches, this method is specifically designed to exploit this functional scenario for point cloud matching. Our new method merges a data-driven approach to compute functional basis and descriptors on the shape's surface and a new refinement method designed for the learned basis. This refinement algorithm arises from a different way of converting functional operators into point-to-point correspondence, which we prove to promote bijectivity between maps, exploiting a theoretical result. Iterating this procedure and performing basis upsampling in the same way as other similar methods, ours increases the accuracy of the correspondence, leading to more bijective correspondences. Different from other approaches, our method allows us to train a functional basis, considering the refinement stage. Combining our new pipeline with an improved feature extractor, our solution outperforms previous methods in various evaluations and settings. We test our method over different datasets, comprising near-isometric and non-isometric pairs. [Display omitted] • We propose a new method for point cloud data-driven correspondences. • We theoretically prove some properties of a new conversion method for the Functional Map method. • We design a novel pipeline which merges the learning of embeddings for point cloud matching a refinement algorithm for bijective correspondences. • We train PointNet++ to compute embedding for point-cloud matching. [ABSTRACT FROM AUTHOR]

Signed distance fields (SDFs) have emerged as an alternative shape representation for real-time collision detection and lighting effects. Computing these for complex models can be expensive, so one popular approach is to prepare an approximation via sampling and interpolation. Then, these may be rendered using sphere marching, which gets close to the surface quickly, but needs several iterations to converge to it. In this paper, we propose an alternative that computes the intersection of a given ray and the surface analytically at a narrow band. This may be combined with other enhancements like having variable error for the approximation depending on the distance to the surface and skipping regions that do not contain the surface to accelerate the outer band ray traversal while reducing the required memory. To achieve smoother representations with minimal computational cost, we propose a method for computing surface intersections and normals from separate interpolants. We evaluate all these to find the optimal combination improving the rendering performance and memory consumption of these SDF approximations. [Display omitted] • Enhancements for rendering SDF piecewise approximations. • Solving narrow band intersections using analytical methods. • Reducing SDF approximations for improving required memory and outer band ray traversal. [ABSTRACT FROM AUTHOR]

Reconstructing High Dynamic Range (HDR) video from alternating exposure Low Dynamic Range (LDR) sequence is an exceptionally challenging task. It not only demands the reliable reconstruction of missing information caused by occlusion or motion without introducing artifacts but also balances the exposure differences between frames to ensure a visually pleasing reconstructed HDR video. Unfortunately, existing methods are typically complex and struggle with unavoidable artifacts and noise, especially when dealing with low-exposed scenes. To tackle this formidable challenge, we propose a two-stage HDR video reconstruction method that employs a global to local alignment strategy. Firstly, we utilize iterative optical flow estimation and hybrid weighting to achieve global alignment, ensuring well-reconstructed in majority of areas. Secondly, the recursive refinement network further addresses locally misaligned areas, reconstructing HDR frames from bottom to top and recursively refining them to yield faithful reconstruction results. Extensive experimental results demonstrate that our method generates the HDR video with fine details and superior visually, surpassing the state-of-the-art method across diverse scenes. [Display omitted] • This paper offers an HDR video reconstruction method that alleviates the inevitable ghosting and noise encountered in existing techniques when dealing with alternating exposure frames. • An innovative strategy that integrates global and local alignment, representing a significant improvement over the single alignment strategy. • A recursive refinement network that produces HDR frames by interacting multi-scale features of input and reconstructed, surpassing the common methods that rely on feature stacking. • The proposed method achieves SOTA performance compared to other competing methods, with the fewest parameters and the fastest inference time. [ABSTRACT FROM AUTHOR]

In this paper, we present an algebraic framework that can be used to construct a large class of 3D shapes and structures that can potentially provide unusual material properties. We formalized this framework as a 3D generalization of planar nonwoven textile structures that are used to mimic the woven structures. Our extension is based on the fact that it is straightforward to extend planar nonwoven textile structures into volumetric nonwoven textile structures, which we also call nonwoven volumetric fabrics. This property is essential because such an extension is impossible with planar woven structures. In other words, using this approach, it can be possible to easily produce volumetric structures that mimic the fabric behavior as if they were planar nonwoven textile structures, which is impossible to produce. These volumetric structures also correspond to regular & semiregular frame structures and are capable of representing previously unknown infinite regular polyhedra and flexible wood structures. [Display omitted] • We present a shape algebra using an extension of p-adic numbers to represent and design volumetric nonwoven structures. • This algebra leads to simple and powerful and topologically robust algorithms for modeling volumetric nonwoven structures. • We developed a tensor-based binary numbering scheme to uniquely describe connections. • We develop an algebraic framework by introducing 2 K -adic and 8 K -adic zeros (binary and octal). • We identified algebraic properties of 2 K -adic and 8 K -adic zeros and demonstrated how to use these properties to produce symmetric nonwoven structures. [ABSTRACT FROM AUTHOR]

Point cloud registration methods based on Gaussian Mixture Models (GMMs) exhibit high robustness. However, GMM cannot precisely depict point clouds, because the Gaussian distribution is spatially symmetric and local surfaces of point clouds are typically non-symmetric. In this paper, we propose a novel method for rigid point cloud registration, termed coherent point drift with Skewed Distribution (Skewed CPD). Our method employs an asymmetric distribution constructed from the local surface normals and curvature radii. Compared to the Gaussian distribution, this skewed distribution provides a more accurate spatial description of points on local surfaces. Additionally, we integrate an adaptive multiplier to the covariance, which reallocates the weight of the covariance for different components in the probabilistic mixture model. We employ the EM algorithm to address this maximum likelihood estimation (MLE) issue and leverage GPU acceleration. In the M-step, we adopt an unconstrained optimization technique rooted in a Lie group and Lie algebra to attain the optimal transformation. Experimental results indicate that our method outperforms state-of-the-art methods in both accuracy and robustness. Remarkably, even without loop closure detection, the cumulative error of our approach remains minimal. • Substitute Gaussian distribution with skewed distribution for enhanced accuracy in probability models. • Construct the probability density function using local surface normals and curvature radii. • Apply an adaptive multiplier to each probability component's covariance. • Utilize Lie-group-based unconstrained optimization for the M-step in the EM algorithm. • Without loop closure detection, outperform other advanced registration methods in cumulative error. [ABSTRACT FROM AUTHOR]

Facial retargeting is a widely used technique in the game and film industries that replicates the expressions of a source facial model onto a target model. Existing methods for facial retargeting rely on either hand-crafted uniform triangle meshes or sparse points obtained from motion capture(mocap). In this paper, we propose an end-to-end facial retargeting algorithm that copies facial expressions from unordered dense point clouds onto the target model. First, a corresponding building method based on bi-harmonic function is introduced to ensure that the template model and a cluster of point clouds share the same triangle topology. Second, a deformation transferring method is presented to transfer the calculated deformation onto the target model. Several experiments are conducted on the SIAT-3DFE dataset to demonstrate the accuracy and efficiency of our method. • We developed a method that leverages bi-harmonic functions to accurately warp the template model into a cluster of unordered point clouds. • We put forward a robust deformation transferring approach that effectively transfers the source facial expression onto the target model. • We conduct several quantitative and qualitative experimental results for our proposed non-rigid registration and deformation transferring method. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel end-to-end variational generative model that utilizes graph-based latent representations for indoor scene synthesis at one time. In contrast to prior research, our method deviates from the practice of gradually introducing and arranging furniture in an empty room using autoregression. Instead, it focuses on acquiring a comprehensive implicit representation of the room's original architectural structure and the placement of furniture. We initially transform the 3D room scene into a dense scene graph, where nodes correspond to the objects present in the room while edges reflect the spatial location links and functional correlations between objects. Then, a neural network is trained to acquire the graph-based latent representation of the room scene through iterative message passing, ultimately resulting in the acquisition of the data distribution on the latent space of the room layout. Given the architectural structure of an empty room as a prerequisite for scene synthesis, the generative model has the ability to sample from the prior distribution of the room's latent representation. This allows the model to then decode and generate a variety of room layouts. We evaluate our method with the state-of-the-art 3D indoor scene dataset and generation methods. The experimental results demonstrate that our method achieves more rational and diverse outcomes in the context of generating scenes under specific conditions. • We propose an end-to-end model for 3D indoor scene synthesis using graph latents. • Unlike floor plans, our method uses graph-based wall nodes for stronger constraints. • Newly latent representations provide function and semantic guidance for optimization. [ABSTRACT FROM AUTHOR]

This paper introduces a novel approach to compute the geometric kernel of a polygon mesh embedded in 3D. The geometric kernel defines the set of points inside or on the shape's boundary, ensuring visibility of the entire shape. The proposed method utilizes scattered rays to identify a sufficient number of sample points on the kernel surface and subsequently leverages these points to locate as many surface vertices as possible. By computing the convex hull of these identified points, we derive an approximation of the kernel. Notably, the output of our method consists exclusively of interior or boundary points of the actual kernel. Comparative evaluations against established CGAL and Polyhedron Kernel algorithms highlight our method's superior computational speed and high approximation accuracy. The parametric structure of our solution allows for different levels of accuracy to be obtained, enabling the user to tailor the approximation to their specific needs. This property sets our algorithm apart from others and provides greater flexibility in its use. Additionally, adjusting the algorithmic settings also enables the computation of the kernel itself with a trade-off in computational speed. Furthermore, our algorithm swiftly and accurately identifies an empty kernel for non-star-shaped configurations. [Display omitted] • Geometric kernel: the convex set of points from which the entire object is visible. • Kernel computation using ray sampling and vertex identification via plane intersections. • Adjusting parameters, rays and recursive search depth, to balance accuracy and speed. • Adaptable method for kernel computation in dynamic shape quality or mesh simplification. • Kernel estimation faster than CGAL and Polyhedron Kernel, providing high accuracy. [ABSTRACT FROM AUTHOR]

Time-varying connectivity of triangle mesh sequences leads to substantial difficulties in their processing. Unlike editing sequences with constant connectivity, editing sequences with varying connectivity requires addressing the problem of temporal correspondence between the frames of the sequence. We present a method for time-consistent editing of triangle mesh sequences with varying connectivity using sparse temporal correspondence, which can be obtained using existing methods. Our method includes a deformation model based on the usage of the sparse temporal correspondence, which is suitable for the temporal propagation of user-specified deformations of the edited surface with respect to the shape and true topology of the surface while preserving the individual connectivity of each frame. Since there is no other method capable of comparable types of editing on time-varying meshes, we compare our method and the proposed deformation model with a baseline approach and demonstrate the benefits of our framework. [Display omitted] • This paper presents a method for editing mesh sequences with varying connectivity. • The proposed method exploits sparse temporal correspondences. • Dual quaternions are used to represent the transformations. [ABSTRACT FROM AUTHOR]

In this paper, we present a systematic review of the applications of (1) Extended Reality (XR), (2) Augmented Reality (AR), and (3) Virtual Reality (VR) technologies to enhance emotion recognition and emotion expression in people with Autism Spectrum Disorder (ASD). ASD can affect various abilities, and poses challenges to the recognition of emotions in others, which is often referred to as "social blindness". Treating this condition typically requires intensive one-on-one or small-group therapy sessions, which can be costly and limited in terms of availability. With the growing number of diagnoses of ASD, concerns have risen regarding a potential "lost generation" that may face difficulties in fulfilling its potential. Through this comprehensive review, we aim to provide an overview of innovative approaches that use XR technologies to improve the learning experience of individuals with ASD. [Display omitted] • Review revealed high interest in XR technologies and wellbeing. • XR technologies significantly improved emotion skills learning. • XR applications greatly enhance user motivation. • A user-centered approach is crucial for maximizing learning efficiency. • Further long-term studies and gamification are needed to sustain motivation. [ABSTRACT FROM AUTHOR]

Generalized Bézier patches with curved domains can represent complex, multi-sided surfaces, but do not provide explicit control over the interior of the surface, as they are defined by means of side-based ribbons. In this paper we extend this representation by proposing a uniform, intuitive control structure, based on templates – a collection of quadrilaterals that covers and affects the 3D shape. It is constructed based on a variant of the Medial Axis Transform (MAT) that uses the local parameterization of the domain. For a given patch a hierarchical sequence of 2D templates can be defined, each determining the topology of the corresponding 3D control structure. First we introduce templates, then present the way of associating biparametric Bernstein blend functions with the control points. Next we describe how to position the control points of the MAT skeleton and the remaining interior control points, while ribbons are preserved. Finally we show a few examples that demonstrate the method and discuss the pros and cons of the approach. • Intuitive Bézier-like control structure. • Support for general topology multi-sided patches. • Construction based on templates computed from medial axis. • Bernstein-based blending functions. • Techniques for interactive editing. [ABSTRACT FROM AUTHOR]