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We propose an efficient method to model paper tearing in the context of interactive modeling. The method uses geometrical information to automatically detect potential starting points of tears. We further introduce a new hybrid geometrical and physical-based method to compute the trajectory of tears while procedurally synthesizing high resolution details of the tearing path using a texture based approach. The results obtained are compared with real paper and with previous studies on the expected geometric paths of paper that tears. [ABSTRACT FROM AUTHOR]

Due to the widespread applications of curves on n‐dimensional spheres, fitting curves on n‐dimensional spheres has received increasing attention in recent years. However, due to the non‐Euclidean nature of spheres, curve fitting methods on n‐dimensional spheres often struggle to balance fitting accuracy and curve fairness. In this paper, we propose a new fitting framework, GLS‐PIA, for parameterized point sets on n‐dimensional spheres to address the challenge. Meanwhile, we provide the proof of the method. Firstly, we propose a progressive iterative approximation method based on geodesic least squares which can directly optimize the geodesic least squares loss on the n‐sphere, improving the accuracy of the fitting. Additionally, we use an error allocation method based on contribution coefficients to ensure the fairness of the fitting curve. Secondly, we propose an adaptive knot placement method based on geodesic difference to estimate a more reasonable distribution of control points in the parameter domain, placing more control points in areas with greater detail. This enables B‐spline curves to capture more details with a limited number of control points. Experimental results demonstrate that our framework achieves outstanding performance, especially in handling imbalanced data points. (In this paper, “sphere” refers to n‐sphere (n ≥ 2) unless otherwise specified.) [ABSTRACT FROM AUTHOR]

Recognizing the detailed information embedded in rasterized floorplans is at the research forefront in the community of computer graphics and vision. With the advent of deep neural networks, automatic floorplan recognition has made tremendous breakthroughs. However, co‐recognizing both the structures and semantics of floorplans through one neural network remains a significant challenge. In this paper, we introduce a novel framework Raster‐to‐Graph, which automatically achieves structural and semantic recognition of floorplans. We represent vectorized floorplans as structural graphs embedded with floorplan semantics, thus transforming the floorplan recognition task into a structural graph prediction problem. We design an autoregressive prediction framework using the neural network architecture of the visual attention Transformer, iteratively predicting the wall junctions and wall segments of floorplans in the order of graph traversal. Additionally, we propose a large‐scale floorplan dataset containing over 10,000 real‐world residential floorplans. Our autoregressive framework can automatically recognize the structures and semantics of floorplans. Extensive experiments demonstrate the effectiveness of our framework, showing significant improvements on all metrics. Qualitative and quantitative evaluations indicate that our framework outperforms existing state‐of‐the‐art methods. Code and dataset for this paper are available at: https://github.com/HSZVIS/Raster-to-Graph. [ABSTRACT FROM AUTHOR]

In this paper, we present a new workflow for the computer‐aided generation of physicalizations, addressing Nested configurations in anatomical and biological structures. Physicalizations are an important component of anatomical and biological education and edutainment. However, existing approaches have mainly revolved around creating data sculptures through digital fabrication. Only a few recent works proposed computer‐aided pipelines for generating sculptures, such as papercrafts, with affordable and readily available materials. Papercraft generation remains a Challenging topic by itself. Yet, anatomical and biological applications pose additional Challenges, such as reconstruction complexity and insufficiency to account for multiple, Nested structures—often present in anatomical and biological structures. Our workflow comprises the following steps: (i) define the Nested configuration of the model and detect its levels, (ii) calculate the viewpoint that provides optimal, unobstructed views on inner levels, (iii) perform cuts on the outer levels to reveal the inner ones based on the viewpoint selection, (iv) estimate the stability of the cut papercraft to ensure a reliable outcome, (v) generate textures at each level, as a smart visibility mechanism that provides additional information on the inner structures, and (vi) unfold each textured mesh guaranteeing reconstruction. Our novel approach exploits the interactivity of Nested papercraft models for edutainment purposes. [ABSTRACT FROM AUTHOR]

Paper pop-ups are interesting three-dimensional books that fascinate people of all ages. The design and construction of these pop-up books however are done manually and require a lot of time and effort. This has led to computer-assisted or automated tools for designing paper pop-ups. This paper proposes an approach for automatically converting a 3D model into a multi-style paper pop-up. Previous automated approaches have only focused on single-style pop-ups, where each is made of a single type of pop-up mechanisms. In our work, we combine multiple styles in a pop-up, which is more representative of actual artist's creations. Our method abstracts a 3D model using suitable primitive shapes that both facilitate the formation of the considered pop-up mechanisms and closely approximate the input model. Each shape is then abstracted using a set of 2D patches that combine to form a valid pop-up. We define geometric conditions that ensure the validity of the combined pop-up structures. In addition, our method also employs an image-based approach for producing the patches to preserve the textures, finer details and important contours of the input model. Finally, our system produces a printable design layout and decides an assembly order for the construction instructions. The feasibility of our results is verified by constructing the actual paper pop-ups from the designs generated by our system. [ABSTRACT FROM AUTHOR]

Information visualization as a field is growing rapidly in popularity since the first information visualization conference in 1995. However, as a consequence of its growth, it is increasingly difficult to follow the growing body of literature within the field. Survey papers and literature reviews are valuable tools for managing the great volume of previously published research papers, and the quantity of survey papers in visualization has reached a critical mass. To this end, this survey paper takes a quantum step forward by surveying and classifying literature survey papers in order to help researchers understand the current landscape of Information Visualization. It is, to our knowledge, the first survey of survey papers (SoS) in Information Visualization. This paper classifies survey papers into natural topic clusters which enables readers to find relevant literature and develops the first classification of classifications. The paper also enables researchers to identify both mature and less developed research directions as well as identify future directions. It is a valuable resource for both newcomers and experienced researchers in and outside the field of Information Visualization and Visual Analytics. [ABSTRACT FROM AUTHOR]

As virtual worlds continue to rise in popularity, so do the expectations of users for the content of virtual scenes. Virtual worlds must be large in scope and offer enough freedom of movement to keep the audience occupied at all times. For content creators, it is difficult to keep up by manually producing the surrounding content. Therefore, the application of procedural modelling techniques is required. Virtual worlds often mimic the real world, which is composed of organized and connected outdoor and indoor layouts. It is expected that all content is present on the virtual scene and that a user can navigate streets, enter buildings, and interact with furniture within a single virtual world. While there are many procedural methods for generating different layout types, they mostly focus only on one layout type, whereas complete scene generation is greatly underrepresented. This paper aims to identify the coverage of layout types by different methods because similar issues exist for the generation of content of different layout types. When creating a new method for layout generation, it is important to know if the results of existing methods can be appended to other methods. This paper presents a survey of existing procedural modelling methods, which were organized into five categories based on the core approach: pure subdivision, grammar‐based, data‐driven, optimization, and simulation. Information about the covered layout types, the possibility of user interaction during the generation process, and the input and output shape types of the generated content is provided for each surveyed method. The input and output shape types of the generated content can be useful to identify which methods can continue the generation by using the output of other methods as their input. It was concluded that all surveyed methods work for only a few different layout types simultaneously. Moreover, only 35% of the surveyed methods offer interaction with the user after completing the initial process of space generation. Most existing approaches do not perform transformations of shape types. A significant number of methods use the irregular shape type as input and generate the same shape type as the output, which is sufficient for coverage of all layout types when generating a complete virtual world. [ABSTRACT FROM AUTHOR]

In machine learning and computer graphics, a fundamental task is the approximation of a probability density function through a well‐dispersed collection of samples. Providing a formal metric for measuring the distance between probability measures on general spaces, Optimal Transport (OT) emerges as a pivotal theoretical framework within this context. However, the associated computational burden is prohibitive in most real‐world scenarios. Leveraging the simple structure of OT in 1D, Sliced Optimal Transport (SOT) has appeared as an efficient alternative to generate samples in Euclidean spaces. This paper pushes the boundaries of SOT utilization in computational geometry problems by extending its application to sample densities residing on more diverse mathematical domains, including the spherical space 핊d, the hyperbolic plane ℍd, and the real projective plane ℙd. Moreover, it ensures the quality of these samples by achieving a blue noise characteristic, regardless of the dimensionality involved. The robustness of our approach is highlighted through its application to various geometry processing tasks, such as the intrinsic blue noise sampling of meshes, as well as the sampling of directions and rotations. These applications collectively underscore the efficacy of our methodology. [ABSTRACT FROM AUTHOR]

Generating realistic animated virtual humans is a problem that has been extensively studied with many applications in different types of virtual environments. However, the creation process of such realistic animations is challenging, especially because of the number and variety of influencing factors, that should then be identified and evaluated. In this paper, we attempt to provide a clearer understanding of how the multiple factors that have been studied in the literature impact the level of realism of animated virtual humans, by providing a survey of studies assessing their realism. This includes a review of features that have been manipulated to increase the realism of virtual humans, as well as evaluation approaches that have been developed. As the challenges of evaluating animated virtual humans in a way that agrees with human perception are still active research problems, this survey further identifies important open problems and directions for future research. [ABSTRACT FROM AUTHOR]

Point‐based radiance field rendering has demonstrated impressive results for novel view synthesis, offering a compelling blend of rendering quality and computational efficiency. However, also latest approaches in this domain are not without their shortcomings. 3D Gaussian Splatting [KKLD23] struggles when tasked with rendering highly detailed scenes, due to blurring and cloudy artifacts. On the other hand, ADOP [RFS22] can accommodate crisper images, but the neural reconstruction network decreases performance, it grapples with temporal instability and it is unable to effectively address large gaps in the point cloud. In this paper, we present TRIPS (Trilinear Point Splatting), an approach that combines ideas from both Gaussian Splatting and ADOP. The fundamental concept behind our novel technique involves rasterizing points into a screen‐space image pyramid, with the selection of the pyramid layer determined by the projected point size. This approach allows rendering arbitrarily large points using a single trilinear write. A lightweight neural network is then used to reconstruct a hole‐free image including detail beyond splat resolution. Importantly, our render pipeline is entirely differentiable, allowing for automatic optimization of both point sizes and positions.Our evaluation demonstrate that TRIPS surpasses existing state‐of‐the‐art methods in terms of rendering quality while maintaining a real‐time frame rate of 60 frames per second on readily available hardware. This performance extends to challenging scenarios, such as scenes featuring intricate geometry, expansive landscapes, and auto‐exposed footage. The project page is located at: https://lfranke.github.io/trips [ABSTRACT FROM AUTHOR]

Most real‐time rendering models for atmospheric effects have been designed and optimized for Earth's atmosphere. Some authors have proposed approaches for rendering other atmospheres, but these methods still use approximations that are only valid on Earth. For instance, the iconic blue glow of Martian sunsets can not be represented properly as the complex interference effects of light scattered at dust particles can not be captured by these approximations. In this paper, we present an approach for generalizing an existing model to make it capable of rendering extraterrestrial atmospheres. This is done by replacing the approximations with a physical model based on Mie Theory. We use the particle‐size distribution, the particle‐density distribution as well as the wavelength‐dependent refractive index of atmospheric particles as input. To demonstrate the feasibility of this idea, we extend the model by Bruneton et al. [BN08] and implement it into CosmoScout VR, an open‐source visualization of our Solar System. In a first step, we use Mie Theory to precompute the scattering behaviour of a particle mixture. Then, multi‐scattering is simulated, and finally the precomputation results are used for real‐time rendering. We demonstrate that this not only improves the visualization of the Martian atmosphere, but also creates more realistic results for our own atmosphere. [ABSTRACT FROM AUTHOR]

The field of visual computing is rapidly advancing due to the emergence of generative artificial intelligence (AI), which unlocks unprecedented capabilities for the generation, editing, and reconstruction of images, videos, and 3D scenes. In these domains, diffusion models are the generative AI architecture of choice. Within the last year alone, the literature on diffusion‐based tools and applications has seen exponential growth and relevant papers are published across the computer graphics, computer vision, and AI communities with new works appearing daily on arXiv. This rapid growth of the field makes it difficult to keep up with all recent developments. The goal of this state‐of‐the‐art report (STAR) is to introduce the basic mathematical concepts of diffusion models, implementation details and design choices of the popular Stable Diffusion model, as well as overview important aspects of these generative AI tools, including personalization, conditioning, inversion, among others. Moreover, we give a comprehensive overview of the rapidly growing literature on diffusion‐based generation and editing, categorized by the type of generated medium, including 2D images, videos, 3D objects, locomotion, and 4D scenes. Finally, we discuss available datasets, metrics, open challenges, and social implications. This STAR provides an intuitive starting point to explore this exciting topic for researchers, artists, and practitioners alike. [ABSTRACT FROM AUTHOR]

Collaboration in extended reality (XR) environments presents complex challenges that revolve around how users perceive the presence, intentions, and actions of their collaborators. This paper delves into the intricate realm of group awareness, focusing specifically on workspace awareness and the innovative visual cues designed to enhance user comprehension. The research begins by identifying a spectrum of collaborative situations drawn from an analysis of XR prototypes in the existing literature. Then, we describe and introduce a novel classification for workspace awareness, along with an exploration of visual cues recently employed in research endeavors. Lastly, we present the key findings and shine a spotlight on promising yet unexplored topics. This work not only serves as a reference for experienced researchers seeking to inform the design of their own collaborative XR applications but also extends a welcoming hand to newcomers in this dynamic field. [ABSTRACT FROM AUTHOR]

Full‐body avatar presence is important for immersive social and environmental interactions in digital reality. However, current devices only provide three six degrees of freedom (DOF) poses from the headset and two controllers (i.e. three‐point trackers). Because it is a highly under‐constrained problem, inferring full‐body pose from these inputs is challenging, especially when supporting the full range of body proportions and use cases represented by the general population. In this paper, we propose a deep learning framework, DivaTrack, which outperforms existing methods when applied to diverse body sizes and activities. We augment the sparse three‐point inputs with linear accelerations from Inertial Measurement Units (IMU) to improve foot contact prediction. We then condition the otherwise ambiguous lower‐body pose with the predictions of foot contact and upper‐body pose in a two‐stage model. We further stabilize the inferred full‐body pose in a wide range of configurations by learning to blend predictions that are computed in two reference frames, each of which is designed for different types of motions. We demonstrate the effectiveness of our design on a large dataset that captures 22 subjects performing challenging locomotion for three‐point tracking, including lunges, hula‐hooping, and sitting. As shown in a live demo using the Meta VR headset and Xsens IMUs, our method runs in real‐time while accurately tracking a user's motion when they perform a diverse set of movements. [ABSTRACT FROM AUTHOR]

We present a computational framework for modeling land parcels and streets. In the real world, parcels and streets are highly coupled with each other since a street network connects all the parcels in a certain area. However, existing works model parcels and streets separately to simplify the problem, resulting in urban layouts with irregular parcels and/or suboptimal streets. In this paper, we propose a hierarchical approach to co‐generate parcels and streets from a user‐specified polygonal land shape, guided by a set of fundamental urban design requirements. At each hierarchical level, new parcels are generated based on binary splitting of existing parcels, and new streets are subsequently generated by leveraging efficient graph search tools to ensure that each new parcel has a street access. At the end, we optimize the geometry of the generated parcels and streets to further improve their geometric quality. Our computational framework outputs an urban layout with a desired number of regular parcels that are reachable via a connected street network, for which users are allowed to control the modeling process both locally and globally. Quantitative comparisons with state‐of‐the‐art approaches show that our framework is able to generate parcels and streets that are superior in some aspects. [ABSTRACT FROM AUTHOR]

Explosive volcanic eruptions rank among the most terrifying natural phenomena, and are thus frequently depicted in films, games, and other media, usually with a bespoke once‐off solution. In this paper, we introduce the first general‐purpose model for bi‐directional interaction between the atmosphere and a volcano plume. In line with recent interactive volcano models, we approximate the plume dynamics with Lagrangian disks and spheres and the atmosphere with sparse layers of 2D Eulerian grids, enabling us to focus on the transfer of physical quantities such as temperature, ash, moisture, and wind velocity between these sub‐models. We subsequently generate volumetric animations by noise‐based procedural upsampling keyed to aspects of advection, convection, moisture, and ash content to generate a fully‐realized volcanic skyscape. Our model captures most of the visually salient features emerging from volcano‐sky interaction, such as windswept plumes, enmeshed cap, bell and skirt clouds, shockwave effects, ash rain, and sheathes of lightning visible in the dark. [ABSTRACT FROM AUTHOR]

Designing effective camera trajectories in virtual 3D environments is a challenging task even for experienced animators. Despite an elaborate film grammar, forged through years of experience, that enables the specification of camera motions through cinematographic properties (framing, shots sizes, angles, motions), there are endless possibilities in deciding how to place and move cameras with characters. Dealing with these possibilities is part of the complexity of the problem. While numerous techniques have been proposed in the literature (optimization‐based solving, encoding of empirical rules, learning from real examples,…), the results either lack variety or ease of control.In this paper, we propose a cinematographic camera diffusion model using a transformer‐based architecture to handle temporality and exploit the stochasticity of diffusion models to generate diverse and qualitative trajectories conditioned by high‐level textual descriptions. We extend the work by integrating keyframing constraints and the ability to blend naturally between motions using latent interpolation, in a way to augment the degree of control of the designers. We demonstrate the strengths of this text‐to‐camera motion approach through qualitative and quantitative experiments and gather feedback from professional artists. The code and data are available at https://github.com/jianghd1996/Camera-control. [ABSTRACT FROM AUTHOR]

Terrain generation methods have long been divided between procedural and physically‐based. Procedural methods build upon the fast evaluation of a mathematical function but suffer from a lack of geological consistency, while physically‐based simulation enforces this consistency at the cost of thousands of iterations unraveling the history of the landscape. In particular, the simulation of the competition between tectonic uplift and fluvial erosion expressed by the stream power law raised recent interest in computer graphics as this allows the generation and control of consistent large‐scale mountain ranges, albeit at the cost of a lengthy simulation. In this paper, we explore the analytical solutions of the stream power law and propose a method that is both physically‐based and procedural, allowing fast and consistent large‐scale terrain generation. In our approach, time is no longer the stopping criterion of an iterative process but acts as the parameter of a mathematical function, a slider that controls the aging of the input terrain from a subtle erosion to the complete replacement by a fully formed mountain range. While analytical solutions have been proposed by the geomorphology community for the 1D case, extending them to a 2D heightmap proves challenging. We propose an efficient implementation of the analytical solutions with a multigrid accelerated iterative process and solutions to incorporate landslides and hillslope processes – two erosion factors that complement the stream power law. [ABSTRACT FROM AUTHOR]

High‐fidelity digital human modeling has become crucial in various applications, including gaming, visual effects and virtual reality. Despite the significant impact of eyelashes on facial aesthetics, their reconstruction and modeling have been largely unexplored. In this paper, we introduce the first data‐driven generative model of eyelashes based on semantic features. This model is derived from real data by introducing a new 3D eyelash reconstruction method based on multi‐view images. The reconstructed data is made available which constitutes the first dataset of 3D eyelashes ever published. Through an innovative extraction process, we determine the features of any set of eyelashes, and present detailed descriptive statistics of human eyelashes shapes. The proposed eyelashes model, which exclusively relies on semantic parameters, effectively captures the appearance of a set of eyelashes. Results show that the proposed model enables interactive, intuitive and realistic eyelashes modeling for non‐experts, enriching avatar creation and synthetic data generation pipelines. [ABSTRACT FROM AUTHOR]

Mesh texture synthesis is a key component in the automatic generation of 3D content. Existing learning‐based methods have drawbacks—either by disregarding the shape manifold during texture generation or by requiring a large number of different views to mitigate occlusion‐related inconsistencies. In this paper, we present a novel surface‐aware approach for mesh texture synthesis that overcomes these drawbacks by leveraging the pre‐trained weights of 2D Convolutional Neural Networks (CNNs) with the same architecture, but with convolutions designed for 3D meshes. Our proposed network keeps track of the oriented patches surrounding each texel, enabling seamless texture synthesis and retaining local similarity to classical 2D convolutions with square kernels. Our approach allows us to synthesize textures that account for the geometric content of mesh surfaces, eliminating discontinuities and achieving comparable quality to 2D image synthesis algorithms. We compare our approach with state‐of‐the‐art methods where, through qualitative and quantitative evaluations, we demonstrate that our approach is more effective for a variety of meshes and styles, while also producing visually appealing and consistent textures on meshes. [ABSTRACT FROM AUTHOR]

Many animals, plants or gems exhibit iridescent material appearance in nature. These are due to specific geometric structures at scales comparable to visible wavelengths, yielding so‐called structural colors. The most vivid examples are due to photonic crystals, where a same structure is repeated in one, two or three dimensions, augmenting the magnitude and complexity of interference effects. In this paper, we study the appearance of 1D photonic crystals (repetitive pairs of thin films), also called Bragg mirrors. Previous work has considered the effect of multiple thin films using the classical transfer matrix approach, which increases in complexity when the number of repetitions increases. Our first contribution is to introduce a more efficient closed‐form reflectance formula [Yeh88] for Bragg mirror reflectance to the Graphics community, as well as an approximation that lends itself to efficient spectral integration for RGB rendering. We then explore the appearance of stacks made of rough Bragg layers. Here our contribution is to show that they may lead to a ballistic transmission, significantly speeding up position‐free rendering and leading to an efficient single‐reflection BRDF model. [ABSTRACT FROM AUTHOR]

Bubble diagrams serve as a crucial tool in the field of architectural planning and graphic design. With the surge of Artificial Intelligence Generated Content (AIGC), there has been a continuous emergence of research and development efforts focused on utilizing bubble diagrams for layout design and generation. However, there is a lack of research efforts focused on bubble diagram generation. In this paper, we propose a novel generative model, BubbleFormer, for generating diverse and plausible bubble diagrams. BubbleFormer consists of two improved Transformer networks: NodeFormer and EdgeFormer. These networks generate nodes and edges of the bubble diagram, respectively. To enhance the generation diversity, a VAE module is incorporated into BubbleFormer, allowing for the sampling and generation of numerous high‐quality bubble diagrams. BubbleFormer is trained end‐to‐end and evaluated through qualitative and quantitative experiments. The results demonstrate that BubbleFormer can generate convincing and diverse bubble diagrams, which in turn drive downstream tasks to produce high‐quality layout plans. The model also shows generalization capabilities in other layout generation tasks and outperforms state‐of‐the‐art techniques in terms of quality and diversity. In previous work, bubble diagrams as input are provided by users, and as a result, our bubble diagram generative model fills a significant gap in automated layout generation driven by bubble diagrams, thereby enabling an end‐to‐end layout design and generation. Code for this paper is at https://github.com/cgjiahui/BubbleFormer. [ABSTRACT FROM AUTHOR]

Artistic authoring of 3D environments is a laborious enterprise that also requires skilled content creators. There have been impressive improvements in using machine learning to address different aspects of generating 3D content, such as generating meshes, arranging geometry, synthesizing textures, etc. In this paper we develop a model to generate Bidirectional Reflectance Distribution Functions (BRDFs) from descriptive textual prompts. BRDFs are four dimensional probability distributions that characterize the interaction of light with surface materials. They are either represented parametrically, or by tabulating the probability density associated with every pair of incident and outgoing angles. The former lends itself to artistic editing while the latter is used when measuring the appearance of real materials. Numerous works have focused on hypothesizing BRDF models from images of materials. We learn a mapping from textual descriptions of materials to parametric BRDFs. Our model is first trained using a semi‐supervised approach before being tuned via an unsupervised scheme. Although our model is general, in this paper we specifically generate parameters for MDL materials, conditioned on natural language descriptions, within NVIDIA's Omniverse platform. This enables use cases such as real‐time text prompts to change materials of objects in 3D environments such as "dull plastic" or "shiny iron". Since the output of our model is a parametric BRDF, rather than an image of the material, it may be used to render materials using any shape under arbitrarily specified viewing and lighting conditions. [ABSTRACT FROM AUTHOR]

Ninety‐degree pop‐ups are a type of papercraft on which a three‐dimensional (3D) structure pops up when the angle of the base fold is 90°. They are fabricated by cutting and creasing a single sheet of paper. Traditional 90° pop‐ups are limited to 3D shapes only comprising planar shapes because they are made of paper. In this paper, we present novel pop‐ups, fabricatable 90° pop‐ups that employ the 90° pop‐up mechanism, consist of components with curved shapes, and can be fabricatable using a 3D printer. We propose a method for converting a 3D model into a fabricatable 90° pop‐up. The user first interactively designs a layout of pop‐up components, and the system automatically deforms the components using the 3D model. Because the generated pop‐ups contain necessary cuts and folds, no additional assembly process is required. To demonstrate the feasibility of the proposed method, we designed and fabricated various 90° pop‐ups using a 3D printer. [ABSTRACT FROM AUTHOR]

Aiming at obtaining structural information and 3D motion of dynamic scenes, scene flow estimation has been an interest of research in computer vision and computer graphics for a long time. It is also a fundamental task for various applications such as autonomous driving. Compared to previous methods that utilize image representations, many recent researches build upon the power of deep analysis and focus on point clouds representation to conduct 3D flow estimation. This paper comprehensively reviews the pioneering literature in scene flow estimation based on point clouds. Meanwhile, it delves into detail in learning paradigms and presents insightful comparisons between the state‐of‐the‐art methods using deep learning for scene flow estimation. Furthermore, this paper investigates various higher‐level scene understanding tasks, including object tracking, motion segmentation, etc. and concludes with an overview of foreseeable research trends for scene flow estimation. [ABSTRACT FROM AUTHOR]

Computational models, such as simulations, are central to a wide range of fields in science and industry. Those models take input parameters and produce some output. To fully exploit their utility, relations between parameters and outputs must be understood. These include, for example, which parameter setting produces the best result (optimization) or which ranges of parameter settings produce a wide variety of results (sensitivity). Such tasks are often difficult to achieve for various reasons, for example, the size of the parameter space, and supported with visual analytics. In this paper, we survey visual parameter space exploration (VPSE) systems involving spatial and temporal data. We focus on interactive visualizations and user interfaces. Through thematic analysis of the surveyed papers, we identify common workflow steps and approaches to support them. We also identify topics for future work that will help enable VPSE on a greater variety of computational models. [ABSTRACT FROM AUTHOR]

We present a novel appearance model for paper. Based on our appearance measurements for matte and glossy paper, we find that paper exhibits a combination of subsurface scattering, specular reflection, retroreflection, and surface sheen. Classic microfacet and simple diffuse reflection models cannot simulate the double-sided appearance of a thin layer. Our novel BSDF model matches our measurements for paper and accounts for both reflection and transmission properties. At the core of the BSDF model is a method for converting a multi-layer subsurface scattering model (BSSRDF) into a BSDF, which allows us to retain physically-based absorption and scattering parameters obtained from the measurements. We also introduce a method for computing the amount of light available for subsurface scattering due to transmission through a rough dielectric surface. Our final model accounts for multiple scattering, single scattering, and surface reflection and is capable of rendering paper with varying levels of roughness and glossiness on both sides. [ABSTRACT FROM AUTHOR]

This paper attempts to explain the mechanics of writing a research paper in visualization. This serves as a useful starting point for those who have never written a research paper before or have very little previous experience. After all, no one is born knowing how to write one. And yet, there are certain elements, a commonality, that should be found in virtually all good visualization research papers. We give our recommendations as to each section a good research paper consists of as well as what each section contains. This paper itself follows our recommended structure. We believe that paper writing should start with the abstract. The abstract can be approximately 6-12 sentences. It is a difficult starting point, but it forces the author to write down a concise description of what they are researching and what the benefits are. Chances are, if the author cannot start out by writing an abstract, then it is not clear in the author's mind what the paper should be about. Of course, the abstract will be refined and updated during the paper writing process. The abstract should concisely (1) identify the research topic, (2) describe the novelty of the presented work and (3) identify the benefits and advantages that result. [ABSTRACT FROM AUTHOR]

The real world consists of mass phenomena, such as iridescence on thin film and metal oxide layers, that is only explicable by wave optics. Existing research can reproduce such effects with simple point lights or low‐frequency environmental lighting. However, it remains a difficult task to efficiently rendering these effects when near‐field, high‐frequency area lights are involved. This paper presents a high‐fidelity, real‐time rendering algorithm for the iridescence effect under polygonal lights. We introduce a novel set of spherical functions, Monomial‐Gaussians, to accurately fit iridescent materials' reflectance. With a precomputed lookup table, the Monomial‐Gaussians are easily integrated over spherical polygons in linear time. Importance sampling of Monomial‐Gaussians is also supported to efficiently reduce Monte‐Carlo error. Our approach produces accurate renderings of the iridescence effect while still preserving high frame rates. [ABSTRACT FROM AUTHOR]

In this paper, we study rendering of end‐to‐end compressed triangle meshes using modern GPU techniques, in particular, mesh shaders. Our approach allows us to keep unstructured triangle meshes in GPU memory in compressed form and decompress them in shader code just in time for rasterization. Typical previous approaches use a compressed mesh format only for persistent storage and streaming, but must decompress it into GPU memory before submitting it to rendering. In contrast, our approach uses an identical compressed format in both storage and GPU memory. Hence, our compression method effectively reduces the in‐memory requirements of huge triangular meshes and avoids any waiting times on streaming geometry induced by the need for a decompression stage on the CPU. End‐to‐end compression also means that scenes with more geometric detail than previously possible can be made fully resident in GPU memory. Our approach is based on a novel decomposition of meshes into meshlets, i.e. disjoint primitive groups that are compressed individually. Decompression using a mesh shader allows de facto random access on the primitive level, which is important for applications such as selective streaming and fine‐grained visibility computation. We compare our approach to multiple commonly used compressed meshlet formats in terms of required memory and rendering times. The results imply that our approach reduces the required CPU–GPU memory bandwidth, a frequent bottleneck in out‐of‐core rendering. [ABSTRACT FROM AUTHOR]

In this paper, we present a method for creating Escher‐like spherical patterns with regular polyhedron symmetries. Using the generators of the symmetry groups associated with regular polyhedra, we first provide fast algorithms to construct spherical tilings. Then, to obtain Escher‐like patterns, we specify texturing techniques to decorate the resulting tilings. Moreover, we present a strategy to create a novel dynamic effect of Escher‐like kaleidoscopes in which the motifs have a complete body. The method has the advantages of simple implementation, fast calculation, good graphics, and artistic effects, which can be used to create rich elegant spherical patterns. [ABSTRACT FROM AUTHOR]

Mesh denoising is a fundamental yet challenging task. Most of the existing data‐driven methods only consider the zero‐order information (vertex location) and first‐order information (face normal). However, higher‐order geometric information (such as curvature) is more descriptive for the shape of the mesh. Therefore, in order to impose such high‐order information, this paper proposes a novel Curvature‐Driven Multi‐Stream Graph Convolutional Neural Network (CDMS‐Net) architecture. CDMS‐Net has three streams, including curvature stream, face normal stream and vertex stream, where the curvature stream focuses on the high‐order Gaussian curvature information. Moreover, CDMS‐Net proposes a novel block based on residual dense connections, which is used as the core component to extract geometric features from meshes. This innovative design improves the performance of feature‐preserving denoising. The plug‐and‐play modular design makes CDMS‐Net easy to be implemented. Multiple sets of ablation study are carried out to verify the rationality of the CDMS‐Net. Our method establishes new state‐of‐the‐art mesh denoising results on publicly available datasets. [ABSTRACT FROM AUTHOR]

This paper investigates super‐resolution to reduce the number of pixels to render and thus speed up Monte Carlo rendering algorithms. While great progress has been made to super‐resolution technologies, it is essentially an ill‐posed problem and cannot recover high‐frequency details in renderings. To address this problem, we exploit high‐resolution auxiliary features to guide super‐resolution of low‐resolution renderings. These high‐resolution auxiliary features can be quickly rendered by a rendering engine and at the same time provide valuable high‐frequency details to assist super‐resolution. To this end, we develop a cross‐modality transformer network that consists of an auxiliary feature branch and a low‐resolution rendering branch. These two branches are designed to fuse high‐resolution auxiliary features with the corresponding low‐resolution rendering. Furthermore, we design Residual Densely Connected Swin Transformer groups to learn to extract representative features to enable high‐quality super‐resolution. Our experiments show that our auxiliary features‐guided super‐resolution method outperforms both super‐resolution methods and Monte Carlo denoising methods in producing high‐quality renderings. [ABSTRACT FROM AUTHOR]

Task‐incremental learning (Task‐IL) aims to enable an intelligent agent to continuously accumulate knowledge from new learning tasks without catastrophically forgetting what it has learned in the past. It has drawn increasing attention in recent years, with many algorithms being proposed to mitigate neural network forgetting. However, none of the existing strategies is able to completely eliminate the issues. Moreover, explaining and fully understanding what knowledge and how it is being forgotten during the incremental learning process still remains under‐explored. In this paper, we propose KnowledgeDrift, a visual analytics framework, to interpret the network forgetting with three objectives: (1) to identify when the network fails to memorize the past knowledge, (2) to visualize what information has been forgotten, and (3) to diagnose how knowledge attained in the new model interferes with the one learned in the past. Our analytical framework first identifies the occurrence of forgetting by tracking the task performance under the incremental learning process and then provides in‐depth inspections of drifted information via various levels of data granularity. KnowledgeDrift allows analysts and model developers to enhance their understanding of network forgetting and compare the performance of different incremental learning algorithms. Three case studies are conducted in the paper to further provide insights and guidance for users to effectively diagnose catastrophic forgetting over time. [ABSTRACT FROM AUTHOR]

Design studies are an integral method of visualization research with hundreds of instances in the literature. Although taught as a theory, the practical implementation of design studies is often excluded from visualization pedagogy due to the lengthy time commitments associated with such studies. Recent research has addressed this challenge and developed an expedited design study framework, the Design Study "Lite" Methodology (DSLM), which can implement design studies with novice students within just 14 weeks. The framework was developed and evaluated based on five semesters of in‐person data visualization courses with 30 students or less and was implemented in conjunction with Service‐Learning (S‐L). With the growth and popularity of the data visualization field—and the teaching environment created by the COVID‐19 pandemic—more academic institutions are offering visualization courses online. Therefore, in this paper, we strengthen and validate the epistemological foundations of the DSLM framework by testing its (1) adaptability to online learning environments and conditions and (2) scalability to larger classes with up to 57 students. We present two online implementations of the DSLM framework, with and without Service‐Learning (S‐L), to test the adaptability and scalability of the framework. We further demonstrate that the framework can be applied effectively without the S‐L component. We reflect on our experience with the online DSLM implementations and contribute a detailed retrospective analysis using thematic analysis and grounded theory methods to draw valuable recommendations and guidelines for future applications of the framework. This work verifies that DSLM can be used successfully in online classes to teach design study methodology. Finally, we contribute novel additions to the DSLM framework to further enhance it for teaching and learning design studies in the classroom. The preprint and supplementary materials for this paper can be found at https://osf.io/6bjx5/. [ABSTRACT FROM AUTHOR]

We present a novel and effective method for modeling a developable surface to simulate paper bending in interactive and animation applications. The method exploits the representation of a developable surface as the envelope of rectifying planes of a curve in 3D, which is therefore necessarily a geodesic on the surface. We manipulate the geodesic to provide intuitive shape control for modeling paper bending. Our method ensures a natural continuous isometric deformation from a piece of bent paper to its flat state without any stretching. Test examples show that the new scheme is fast, accurate, and easy to use, thus providing an effective approach to interactive paper bending. We also show how to handle non-convex piecewise smooth developable surfaces. [ABSTRACT FROM AUTHOR]

Remote sensing images (RSIs) often possess obvious background noises, exhibit a multi‐scale phenomenon, and are characterized by complex scenes with ground objects in diversely spatial distribution pattern, bringing challenges to the corresponding semantic segmentation. CNN‐based methods can hardly address the diverse spatial distributions of ground objects, especially their compositional relationships, while Vision Transformers (ViTs) introduce background noises and have a quadratic time complexity due to dense global matrix multiplications. In this paper, we introduce Adaptive Pattern Matching (APM), a lightweight method for long‐range adaptive weight aggregation. Our APM obtains a set of pixels belonging to the same spatial distribution pattern of each pixel, and calculates the adaptive weights according to their compositional relationships. In addition, we design a tiny U‐shaped network using the APM as a module to address the large variance of scales of ground objects in RSIs. This network is embedded after each stage in a backbone network to establish a Multi‐stage U‐shaped Adaptive Pattern Matching Network (MAPMaN), for nested multi‐scale modeling of ground objects towards semantic segmentation of RSIs. Experiments on three datasets demonstrate that our MAPMaN can outperform the state‐of‐the‐art methods in common metrics. The code can be available at https://github.com/INiid/MAPMaN. [ABSTRACT FROM AUTHOR]

This paper proposes a novel CPU‐GPU hybrid encoding method based on the ETC2 format, commonly used on mobile platforms. Traditional texture compression techniques often face a trade‐off between encoding speed and quality. For a better trade‐off, our approach utilizes both the CPU and GPU. In a pipeline we designed, the CPU encoder identifies problematic pixel blocks during the encoding process, and the GPU encoder re‐encodes them. Additionally, we carefully improve the base CPU and GPU encoders regarding encoding speed and quality. As a result, our encoder minimizes compression artifacts, increases encoding speed, or achieves both of these goals compared to previous high‐quality offline ETC2 encoders. [ABSTRACT FROM AUTHOR]

Although digital painting has advanced much in recent years, there is still a significant divide between physically drawn paintings and purely digitally drawn paintings. These differences arise due to the physical interactions between the brush, ink, and paper, which are hard to emulate in the digital domain. Most ink painting approaches have focused on either using heuristics or physical simulation to attempt to bridge the gap between digital and analog, however, these approaches are still unable to capture the diversity of painting effects, such as ink fading or blotting, found in the real world. In this work, we propose a data‐driven approach to generate ink paintings based on a semi‐automatically collected high‐quality real‐world ink painting dataset. We use a multi‐camera robot‐based setup to automatically create a diversity of ink paintings, which allows for capturing the entire process in high resolution, including capturing detailed brush motions and drawing results. To ensure high‐quality capture of the painting process, we calibrate the setup and perform occlusion‐aware blending to capture all the strokes in high resolution in a robust and efficient way. Using our new dataset, we propose a recursive deep learning‐based model to reproduce the ink paintings stroke by stroke while capturing complex ink painting effects such as bleeding and mixing. Our results corroborate the fidelity of the proposed approach to real hand‐drawn ink paintings in comparison with existing approaches. We hope the availability of our dataset will encourage new research on digital realistic ink painting techniques. [ABSTRACT FROM AUTHOR]

Recent advancements in technology have brought forth new forms of interactive applications, such as the social metaverse, where end users interact with each other through their virtual avatars. In such applications, precise full‐body tracking is essential for an immersive experience and a sense of embodiment with the virtual avatar. However, current motion capture systems are not easily accessible to end users due to their high cost, the requirement for special skills to operate them, or the discomfort associated with wearable devices. In this paper, we present MOVIN, the data‐driven generative method for real‐time motion capture with global tracking, using a single LiDAR sensor. Our autoregressive conditional variational autoencoder (CVAE) model learns the distribution of pose variations conditioned on the given 3D point cloud from LiDAR. As a central factor for high‐accuracy motion capture, we propose a novel feature encoder to learn the correlation between the historical 3D point cloud data and global, local pose features, resulting in effective learning of the pose prior. Global pose features include root translation, rotation, and foot contacts, while local features comprise joint positions and rotations. Subsequently, a pose generator takes into account the sampled latent variable along with the features from the previous frame to generate a plausible current pose. Our framework accurately predicts the performer's 3D global information and local joint details while effectively considering temporally coherent movements across frames. We demonstrate the effectiveness of our architecture through quantitative and qualitative evaluations, comparing it against state‐of‐the‐art methods. Additionally, we implement a real‐time application to showcase our method in real‐world scenarios. MOVIN dataset is available at https://movin3d.github.io/movin_pg2023/https://movin3d.github.io/movin_pg2023/">https://movin3d.github.io/movin_pg2023/. [ABSTRACT FROM AUTHOR]

Low‐light conditions often result in the presence of significant noise and artifacts in captured images, which can be further exacerbated during the image enhancement process, leading to a decrease in visual quality. This paper aims to present an effective low‐light image enhancement model based on the variation Retinex model that successfully suppresses noise and artifacts while preserving image details. To achieve this, we propose a modified Bilateral Total Variation to better smooth out fine textures in the illuminance component while maintaining weak structures. Additionally, tensor sparse coding is employed as a regularization term to remove noise and artifacts from the reflectance component. Experimental results on extensive and challenging datasets demonstrate the effectiveness of the proposed method, exhibiting superior or comparable performance compared to state‐of‐the‐art approaches. Code, dataset and experimental results are available at https://github.com/YangWeipengscut/BTRetinex. [ABSTRACT FROM AUTHOR]

Color correction for photographed images is an ill‐posed problem. It is also a crucial initial step towards material acquisition for inverse rendering methods or pipelines. Several state‐of‐the‐art methods rely on reducing color differences for imaged reference color chart blocks of known color values to devise or optimize their solution. In this paper, we first establish through simulations the limitation of this minimality criteria which in principle results in overfitting. Next, we study and propose a few spatial distribution measures to augment the evaluation criteria. Thereafter, we propose a novel patch‐based, white‐point centric approach that processes luminance and chrominance information separately to improve on the color matching task. We compare our method qualitatively with several state‐of‐the art methods using our augmented evaluation criteria along with quantitative examinations. Finally, we perform rigorous experiments and demonstrate results to clearly establish the benefits of our proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we propose an efficient approach for the compression and representation of volumetric data utilizing coordinate‐based networks and multi‐resolution hash encoding. Efficient compression of volumetric data is crucial for various applications, such as medical imaging and scientific simulations. Our approach enables effective compression by learning a mapping between spatial coordinates and intensity values. We compare different encoding schemes and demonstrate the superiority of multiresolution hash encoding in terms of compression quality and training efficiency. Furthermore, we leverage optimization‐based meta‐learning, specifically using the Reptile algorithm, to learn weight initialization for neural representations tailored to volumetric data, enabling faster convergence during optimization. Additionally, we compare our approach with state‐of‐the‐art methods to showcase improved image quality and compression ratios. These findings highlight the potential of coordinate‐based networks and multi‐resolution hash encoding for an efficient and accurate representation of volumetric data, paving the way for advancements in large‐scale data visualization and other applications. [ABSTRACT FROM AUTHOR]

Colorizing icon is a challenging task, even for skillful artists, as it involves balancing aesthetics and practical considerations. Prior works have primarily focused on colorizing pixel‐based icons, which do not seamlessly integrate into the current vector‐based icon design workflow. In this paper, we propose a palette‐based colorization algorithm for vector icons without the need for rasterization. Our algorithm takes a vector icon and a five‐color palette as input and generates various colorized results for designers to choose from. Inspired by the common icon design workflow, we developed our algorithm to consist of two steps: generating a colorization template and performing the palette‐based color transfer. To generate the colorization templates, we introduce a novel vector icon colorization model that employs an MRF‐based loss and a color harmony loss. The color harmony loss encourages the alignment of the resulting color template with widely used harmony templates. We then map the predicted colorization template to chroma‐like palette colors to obtain diverse colorization results. We compare our results with those generated by previous pixel‐based icon colorization methods and validate the effectiveness of our algorithm by evaluations in both qualitative and quantitative measurements. Our method enables icon designers to explore diverse colorization results for a single icon using different color palettes while also efficiently evaluating the suitability of a color palette for a set of icons. [ABSTRACT FROM AUTHOR]

Face swapping is a technique that replaces a face in a target media with another face of a different identity from a source face image. Currently, research on the effective utilisation of prior knowledge and semantic guidance for photo‐realistic face swapping remains limited, despite the impressive synthesis quality achieved by recent generative models. In this paper, we propose a novel conditional Denoising Diffusion Probabilistic Model (DDPM) enforced by a two‐level face prior guidance. Specifically, it includes (i) an image‐level condition generated by a 3D Morphable Model (3DMM), and (ii) a high‐semantic level guidance driven by information extracted from several pre‐trained attribute classifiers, for high‐quality face image synthesis. Although swapped face image from 3DMM does not achieve photo‐realistic quality on its own, it provides a strong image‐level prior, in parallel with high‐level face semantics, to guide the DDPM for high fidelity image generation. The experimental results demonstrate that our method outperforms state‐of‐the‐art face swapping methods on benchmark datasets in terms of its synthesis quality, and capability to preserve the target face attributes and swap the source face identity. [ABSTRACT FROM AUTHOR]

Sketch‐based shape modeling aims to bridge the gap between 2D drawing and 3D modeling by providing an intuitive and accessible approach to create 3D shapes from 2D sketches. However, existing methods still suffer from limitations in reconstruction quality and multi‐view interaction friendliness, hindering their practical application. This paper proposes a faithful and user‐friendly iterative solution to tackle these limitations by learning geometry‐aligned deep implicit functions from one or multiple sketches. Our method lifts 2D sketches to volume‐based feature tensors, which align strongly with the output 3D shape, enabling accurate reconstruction and faithful editing. Such a geometry‐aligned feature encoding technique is well‐suited to iterative modeling since features from different viewpoints can be easily memorized or aggregated. Based on these advantages, we design a unified interactive system for sketch‐based shape modeling. It enables users to generate the desired geometry iteratively by drawing sketches from any number of viewpoints. In addition, it allows users to edit the generated surface by making a few local modifications. We demonstrate the effectiveness and practicality of our method with extensive experiments and user studies, where we found that our method outperformed existing methods in terms of accuracy, efficiency, and user satisfaction. The source code of this project is available at https://github.com/LordLiang/GA‐Sketching. [ABSTRACT FROM AUTHOR]

Sketch‐based image retrieval (SBIR) is an emerging task in computer vision. Research interests have arisen in solving this problem under the realistic and challenging setting of zero‐shot learning. Given a sketch as a query, the search goal is to retrieve the corresponding photographs in a zero‐shot scenario. In this paper, we divide the aforementioned challenging work into three tasks and propose a sharing model framework that addresses these problems. First, the weights of the proposed sharing model effectively reduced the modality gap between sketches and photographs. Second, semantic information was used to handle different label spaces during the training and testing stages. The sketch and photograph domains share semantic information. Finally, a memory mechanism is used to reduce the intrinsic variety in sketches, even if they all belong to the same class. Sketches and photographs dominate the embeddings in turn. Because sketches are not limited by language, our ultimate goal is to find a method to replace text searches. We also designed a demonstration program to demonstrate the use of the proposed method in real‐world applications. Our results indicate that the proposed method exhibits considerably higher zero‐shot SBIR performance than do other state‐of‐the‐art methods on the challenging Sketchy, TU‐Berlin, and QuickDraw datasets. [ABSTRACT FROM AUTHOR]

Despite natural image shadow removal methods have made significant progress, they often perform poorly for facial image due to the unique features of the face. Moreover, most learning‐based methods are designed based on pixel‐level strategies, ignoring the global contextual relationship in the image. In this paper, we propose a graph‐based feature fusion network (GraphFFNet) for facial image shadow removal. We apply a graph‐based convolution encoder (GCEncoder) to extract global contextual relationships between regions in the coarse shadow‐less image produced by an image flipper. Then, we introduce a feature modulation module to fuse the global topological relation onto the image features, enhancing the feature representation of the network. Finally, the fusion decoder integrates all the effective features to reconstruct the image features, producing a satisfactory shadow‐removal result. Experimental results demonstrate the superiority of the proposed GraphFFNet over the state‐of‐the‐art and validate the effectiveness of facial image shadow removal. [ABSTRACT FROM AUTHOR]

3D point clouds can represent complex 3D objects of arbitrary topologies and with fine‐grained details. They are, however, hard to regress from images using convolutional neural networks, making tasks such as 3D reconstruction from monocular RGB images challenging. In fact, unlike images and volumetric grids, point clouds are unstructured and thus lack proper parameterization, which makes them difficult to process using convolutional operations. Existing point‐based 3D reconstruction methods that tried to address this problem rely on complex end‐to‐end architectures with high computational costs. Instead, we propose in this paper a novel mechanism that decouples the 3D reconstruction problem from the structure (or parameterization) learning task, making the 3D reconstruction of objects of arbitrary topologies tractable and thus easier to learn. We achieve this using a novel Teacher‐Student network where the Teacher learns to structure the point clouds. The Student then harnesses the knowledge learned by the Teacher to efficiently regress accurate 3D point clouds. We train the Teacher network using 3D ground‐truth supervision and the Student network using the Teacher's annotations. Finally, we employ a novel refinement network to overcome the upper‐bound performance that is set by the Teacher network. Our extensive experiments on ShapeNet and Pix3D benchmarks, and on in‐the‐wild images demonstrate that the proposed approach outperforms previous methods in terms of reconstruction accuracy and visual quality. [ABSTRACT FROM AUTHOR]

Procedural models enable the generation of a large amount of diverse shapes by varying the parameters of the model. However, writing a procedural model for replicating a collection of reference shapes is difficult, requiring much inspection of the original and replicated shapes during the development of the model. In this paper, we introduce a data‐guided method for aiding a programmer in creating a procedural model to replicate a collection of reference shapes. The user starts by writing an initial procedural model, and the system automatically predicts the model parameters for reference shapes, also grouping shapes by how well they are approximated by the current procedural model. The user can then update the procedural model based on the given feedback and iterate the process. Our system thus automates the tedious process of discovering the parameters that replicate reference shapes, allowing the programmer to focus on designing the high‐level rules that generate the shapes. We demonstrate through qualitative examples and a user study that our method is able to speed up the development time for creating procedural models of 2D and 3D man‐made shapes. [ABSTRACT FROM AUTHOR]