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Background: Redirected jumping (RDJ) allows users to explore virtual environments (VEs) naturally by scaling a small real-world jump to a larger virtual jump with virtual camera motion manipulation, thereby addressing the problem of limited physical space in VR applications. Previous RDJ studies have mainly focused on detection threshold estimation. However, the effect VE or selfrepresentation (SR) has on the perception or performance of RDJs remains unclear. Methods: In this paper, we report experiments to measure the perception (detection thresholds for gains, presence, embodiment, intrinsic motivation, and cybersickness) and physical performance (heart rate intensity, preparation time, and actual jumping distance) of redirected forward jumping under six different combinations of VE (low and high visual richness) and SRs (invisible, shoes, and human-like). Results: Our results indicated that the detection threshold ranges for horizontal translation gains were significantly smaller in the VE with high rather than low visual richness. When different SRs were applied, our results did not suggest significant differences in detection thresholds, but it did report longer actual jumping distances in the invisible body case compared with the other two SRs. In the high visual richness VE, the preparation time for jumping with a human-like avatar was significantly longer than that with other SRs. Finally, some correlations were found between perception and physical performance measures. Conclusions: All these findings suggest that both VE and SRs influence users' perception and performance in RDJ and must be considered when designing locomotion techniques.

Background: This study proposes a series of geometry and physics modeling methods for personalized cardiovascular intervention procedures, which can be applied to a virtual endovascular simulator. Methods: Based on personalized clinical computed tomography angiography (CTA) data, mesh models of the cardiovascular system were constructed semi-automatically. By coupling 4D magnetic resonance imaging (MRI) sequences corresponding to a complete cardiac cycle with related physics models, a hybrid kinetic model of the cardiovascular system was built to drive kinematics and dynamics simulation. On that basis, the surgical procedures related to intervention instruments were simulated using specially-designed physics models. These models can be solved in real-time; therefore, the complex interactions between blood vessels and instruments can be well simulated. Additionally, X-ray imaging simulation algorithms and realistic rendering algorithms for virtual intervention scenes are also proposed. In particular, instrument tracking hardware with haptic feedback was developed to serve as the interaction interface of real instruments and the virtual intervention system. Finally, a personalized cardiovascular intervention simulation system was developed by integrating the techniques mentioned above. Results: This system supported instant modeling and simulation of personalized clinical data and significantly improved the visual and haptic immersions of vascular intervention simulation. Conclusions: It can be used in teaching basic cardiology and effectively satisfying the demands of intervention training, personalized intervention planning, and rehearsing.

In virtual reality (VR) applications, hand-controller interaction is largely limited by the biomechanical structure of the arm and its kinematical features. Earlier research revealed that different arm postures generate distinct arm fatigue levels in mid-air operational tasks; however, how they impact interaction performance, e.g., accuracy of target grasp and manipulation, has been less investigated. To fill this gap in knowledge, we conducted an empirical experiment in which thirty participants were recruited to complete a series of target acquisition tasks in a specifically designed VR application. Results show that (1) a bent arm posture resulted in a higher interaction accuracy than a stretched arm posture; (2) a downward arm posture interacted more accurately than an upraised arm posture; since two arms are bilaterally symmetric, (3) either selected arm interacted more accurately on the corresponding side than on the opposite side; and (4) the user-preferred or dominant arm interacted more persistently than the non-dominant one, though two arms generated little difference in interaction accuracy. Implications and suggestions are discussed for designing more efficient and user-satisfying interactive spaces in VR.

Pedestrian detection and human pose estimation are instructive for reconstructing a three-dimensional scenario and for robot navigation, particularly when large amounts of vision data are captured using various data-recording techniques. Using an unrestricted capture scheme, which produces occlusions or breezing, the information describing each part of a human body and the relationship between each part or even different pedestrians must be present in a still image. Using this framework, a multi-layered, spatial, virtual, human pose reconstruction framework is presented in this study to recover any deficient information in planar images. In this framework, a hierarchical parts-based deep model is used to detect body parts by using the available restricted information in a still image and is then combined with spatial Markov random fields to re-estimate the accurate joint positions in the deep network. Then, the planar estimation results are mapped onto a virtual three-dimensional space using multiple constraints to recover any deficient spatial information. The proposed approach can be viewed as a general pre-processing method to guide the generation of continuous, three-dimensional motion data. The experiment results of this study are used to describe the effectiveness and usability of the proposed approach.

Skin synthesis is important for the actual appearance of digital human models. However, it is difficult to design a general algorithm to efficiently produce high quality results. This paper proposes a parallel texture synthesis method for large scale skin of digital human models. Two major procedures are included in this method, a parallel matching procedure and a multi-pass optimizing procedure. Compared with other methods, this algorithm is easy to use, requires only a small size of skin image as input, and generates an arbitrary size of skin texture with high quality. As demonstrated by experiments, the effectiveness of this skin texture synthesis method is confirmed. [ABSTRACT FROM AUTHOR]

Existing light field-based lighting follows the representation on plane, such as the commonly used light slab. A novel light field representation on scene surface is proposed. The illumination is captured by a panorama camera on translation stage with a nonuniform sampling and calibration strategy. Sampled light rays are reprojected on the reconstructed scene to generate the scene surface light field representations for both direct light source and indirect light. This representation is more consistent with the actual emission and reflection of light transport than the existing ones. An accurate light source estimation and a practical indirect light resampling approach are presented based on the representation. The rendering results illustrate the realistic lighting effect, especially in spatially varying illumination. [ABSTRACT FROM AUTHOR]

We propose a novel edit propagation algorithm for interactive image and video manipulations. Our approach uses the locally linear embedding (LLE) to represent each pixel as a linear combination of its neighbors in a feature space. While previous methods require similar pixels to have similar results, we seek to maintain the manifold structure formed by all pixels in the feature space. Specifically, we require each pixel to be the same linear combination of its neighbors in the result. Compared with previous methods, our proposed algorithm is more robust to color blending in the input data. Furthermore, since every pixel is only related to a few nearest neighbors, our algorithm easily achieves good runtime efficiency. We demonstrate our manifold preserving edit propagation on various applications. [ABSTRACT FROM AUTHOR]

Rigging for facial animation is an important but time-consuming task, which generally requires experienced artists with knowledge of facial anatomy. In this paper, we investigate whether it is possible to produce a good animatable avatar automatically, given only a 3D static triangle mesh of the head. An automatic mechanism is devised for constructing multi-layer animatable facial avatars for unseen faces. We evaluate our technique with a variety of models, and give a quantitative analysis of the constructed results. We also designed and conducted a user study for evaluating the perceived quality of the generated expressive animations. The results demonstrate that our method is an appropriate tool for naïve users to customize their personal 3D avatars. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Abstract  This paper proposes a method for integrating multiple range images obtained by a range scanner, aiming to get an entire model automatically. We extract texture images for all range images and register any pair of them based on image features of their texture images. To verify the correctness and filter out mismatching pairs of range images, multiview matching process searches a model graph for all the consistent cycles. We prove that the consistent cycle space is a linear subspace and the entire cycle space can be decomposed into the direct sum of a consistent cycle space and its orthogonal complementary subspace. Moreover, we present an efficient algorithm for computing a basis of the consistent cycle space based on adjacency-of-cycle-basis graph, which avoids the exponential complexity of exhaustive search. [ABSTRACT FROM AUTHOR]

Workflow management enables grid service composition and user collaboration in grid middleware, it includes the workflow of grid service invoking and data exchanging among services. Because of the large requirements of grid service composition, it's necessary for the workflow management to be scalable in many grid application systems. This paper proposes a distributed workflow management model for grid middleware, it is comprised of three modules including workflow definition tool (WDT), workflow balancer (WB), workflow execution engine (WEE) and defines the interfaces between workflow management and other grid components. Firstly, WDT composites grid services into workflow service, secondly WB dispatches workflow services and workflow job requests, thirdly WEE is up to the execution of workflow jobs. Furthermore, it describes the interfaces between workflow management and related grid components, such as job management, information service and grid portal. Also it divides the interfaces into WEE management, workflow service management and workflow job management. With the implementation and experiments of this model for Chinese education and research, its support platform middleware, its usability is verified through the usage of workflow tools in image processing scenario, its scalability and load balancing ability are proved in workflow scheduling test. [ABSTRACT FROM AUTHOR]

This paper introduces a novel topic model for learning a robust object model. In this hierarchical model, the layout topic is used to capture the local relationships among a limited number of parts when the part topic is used to locate the potential part regions. Naturally, an object model is represented as a probability distribution over a set of parts with certain layouts. Rather than a monolithic model, our object model is composed of multiple sub-category models designed to capture the significant variations in appearance and shape of an object category. Given a set of object instances with a bounding box, an iterative learning process is proposed to divide them into several sub-categories and learn the corresponding sub-category models without any supervision. Through an experiment in object detection, the learned object model is examined and the results highlight the advantages of our present method compared with others. [ABSTRACT FROM AUTHOR]

With the increasing availability of high-resolution images, videos, and 3D models, the demand for scalable large data processing techniques increases. We introduce a method of sparse dictionary learning for edit propagation of large input data. Previous approaches for edit propagation typically employ a global optimization over the whole set of pixels (or vertexes), incurring a prohibitively high memory and time-consumption for large input data. Rather than propagating an edit pixel by pixel, we follow the principle of sparse representation to obtain a representative and compact dictionary and perform edit propagation on the dictionary instead. The sparse dictionary provides an intrinsic basis for input data, and the coding coefficients capture the linear relationship between all pixels and the dictionary atoms. The learned dictionary is then optimized by a novel scheme, which maximizes the Kullback-Leibler divergence between each atom pair to remove redundant atoms. To enable local edit propagation for images or videos with similar appearance, a dictionary learning strategy is proposed by considering range constraint to better account for the global distribution of pixels in their feature space. We show several applications of the sparsity-based edit propagation, including video recoloring, theme editing, and seamless cloning, operating on both color and texture features. Our approach can also be applied to computer graphics tasks, such as 3D surface deformation. We demonstrate that with an atom-to-pixel ratio in the order of 0.01% signifying a significant reduction on memory consumption, our method still maintains a high degree of visual fidelity.