545 research outputs found

    Physics-Based Modeling, Analysis and Animation

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    The idea of using physics-based models has received considerable interest in computer graphics and computer vision research the last ten years. The interest arises from the fact that simple geometric primitives cannot accurately represent natural objects. In computer graphics physics-based models are used to generate and visualize constrained shapes, motions of rigid and nonrigid objects and object interactions with the environment for the purposes of animation. On the other hand, in computer vision, the method applies to complex 3-D shape representation, shape reconstruction and motion estimation. In this paper we review two models that have been used in computer graphics and two models that apply to both areas. In the area of computer graphics, Miller [48] uses a mass-spring model to animate three forms of locomotion of snakes and worms. To overcome the problem of the multitude of degrees of freedom associated with the mass-spring lattices, Witkin and Welch [87] present a geometric method to model global deformations. To achieve the same result Pentland and Horowitz in [54] delineate the object motion into rigid and nonrigid deformation modes. To overcome problems of these two last approaches, Metaxas and Terzopoulos in [45] successfully combine local deformations with global ones. Modeling based on physical principles is a potent technique for computer graphics and computer vision. It is a rich and fruitful area for research in terms of both theory and applications. It is important, though, to develop concepts, methodologies, and techniques which will be widely applicable to many types of applications

    A spring force formulation for elastically deformable models

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    Cataloged from PDF version of article.Continuous deformable models are generally represented using a grid of control points. The elastic properties are then modeled using the interactions between these points. The formulations based on elasticity theory express these interactions using stiffness matrices. These matrices store the elastic properties of the models and they should be evolved in time according to changing elastic properties of the models. However, forming the stiffness matrices at any step of an animation is very difficult and sometimes the differential equations that should be solved to produce animation become ill-conditioned. Instead of modeling the elasticities using stiffness matrices, the interactions between model points could be expressed in terms of external spring forces. In this paper, a spring force formulation for animating elastically deformable models is presented. In this formulation, elastic properties of the materials are represented as external spring forces as opposed to forming complicated stiffness matrices. (C) 1997 Elsevier Science Ltd

    HIGH QUALITY HUMAN 3D BODY MODELING, TRACKING AND APPLICATION

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    Geometric reconstruction of dynamic objects is a fundamental task of computer vision and graphics, and modeling human body of high fidelity is considered to be a core of this problem. Traditional human shape and motion capture techniques require an array of surrounding cameras or subjects wear reflective markers, resulting in a limitation of working space and portability. In this dissertation, a complete process is designed from geometric modeling detailed 3D human full body and capturing shape dynamics over time using a flexible setup to guiding clothes/person re-targeting with such data-driven models. As the mechanical movement of human body can be considered as an articulate motion, which is easy to guide the skin animation but has difficulties in the reverse process to find parameters from images without manual intervention, we present a novel parametric model, GMM-BlendSCAPE, jointly taking both linear skinning model and the prior art of BlendSCAPE (Blend Shape Completion and Animation for PEople) into consideration and develop a Gaussian Mixture Model (GMM) to infer both body shape and pose from incomplete observations. We show the increased accuracy of joints and skin surface estimation using our model compared to the skeleton based motion tracking. To model the detailed body, we start with capturing high-quality partial 3D scans by using a single-view commercial depth camera. Based on GMM-BlendSCAPE, we can then reconstruct multiple complete static models of large pose difference via our novel non-rigid registration algorithm. With vertex correspondences established, these models can be further converted into a personalized drivable template and used for robust pose tracking in a similar GMM framework. Moreover, we design a general purpose real-time non-rigid deformation algorithm to accelerate this registration. Last but not least, we demonstrate a novel virtual clothes try-on application based on our personalized model utilizing both image and depth cues to synthesize and re-target clothes for single-view videos of different people

    Physics-Based Modeling of Nonrigid Objects for Vision and Graphics (Dissertation)

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    This thesis develops a physics-based framework for 3D shape and nonrigid motion modeling for computer vision and computer graphics. In computer vision it addresses the problems of complex 3D shape representation, shape reconstruction, quantitative model extraction from biomedical data for analysis and visualization, shape estimation, and motion tracking. In computer graphics it demonstrates the generative power of our framework to synthesize constrained shapes, nonrigid object motions and object interactions for the purposes of computer animation. Our framework is based on the use of a new class of dynamically deformable primitives which allow the combination of global and local deformations. It incorporates physical constraints to compose articulated models from deformable primitives and provides force-based techniques for fitting such models to sparse, noise-corrupted 2D and 3D visual data. The framework leads to shape and nonrigid motion estimators that exploit dynamically deformable models to track moving 3D objects from time-varying observations. We develop models with global deformation parameters which represent the salient shape features of natural parts, and local deformation parameters which capture shape details. In the context of computer graphics, these models represent the physics-based marriage of the parameterized and free-form modeling paradigms. An important benefit of their global/local descriptive power in the context of computer vision is that it can potentially satisfy the often conflicting requirements of shape reconstruction and shape recognition. The Lagrange equations of motion that govern our models, augmented by constraints, make them responsive to externally applied forces derived from input data or applied by the user. This system of differential equations is discretized using finite element methods and simulated through time using standard numerical techniques. We employ these equations to formulate a shape and nonrigid motion estimator. The estimator is a continuous extended Kalman filter that recursively transforms the discrepancy between the sensory data and the estimated model state into generalized forces. These adjust the translational, rotational, and deformational degrees of freedom such that the model evolves in a consistent fashion with the noisy data. We demonstrate the interactive time performance of our techniques in a series of experiments in computer vision, graphics, and visualization

    Applications of computer-graphics animation for motion-perception research

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    The advantages and limitations of using computer animated stimuli in studying motion perception are presented and discussed. Most current programs of motion perception research could not be pursued without the use of computer graphics animation. Computer generated displays afford latitudes of freedom and control that are almost impossible to attain through conventional methods. There are, however, limitations to this presentational medium. At present, computer generated displays present simplified approximations of the dynamics in natural events. Very little is known about how the differences between natural events and computer simulations influence perceptual processing. In practice, the differences are assumed to be irrelevant to the questions under study, and that findings with computer generated stimuli will generalize to natural events

    DEFORM'06 - Proceedings of the Workshop on Image Registration in Deformable Environments

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    Preface These are the proceedings of DEFORM'06, the Workshop on Image Registration in Deformable Environments, associated to BMVC'06, the 17th British Machine Vision Conference, held in Edinburgh, UK, in September 2006. The goal of DEFORM'06 was to bring together people from different domains having interests in deformable image registration. In response to our Call for Papers, we received 17 submissions and selected 8 for oral presentation at the workshop. In addition to the regular papers, Andrew Fitzgibbon from Microsoft Research Cambridge gave an invited talk at the workshop. The conference website including online proceedings remains open, see http://comsee.univ-bpclermont.fr/events/DEFORM06. We would like to thank the BMVC'06 co-chairs, Mike Chantler, Manuel Trucco and especially Bob Fisher for is great help in the local arrangements, Andrew Fitzgibbon, and the Programme Committee members who provided insightful reviews of the submitted papers. Special thanks go to Marc Richetin, head of the CNRS Research Federation TIMS, which sponsored the workshop. August 2006 Adrien Bartoli Nassir Navab Vincent Lepeti

    Matrix-based Parameterizations of Skeletal Animated Appearance

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    Alors que le rendu réaliste gagne de l’ampleur dans l’industrie, les techniques à la fois photoréalistes et basées sur la physique, complexes en terme de temps de calcul, requièrent souvent une étape de précalcul hors-ligne. Les applications en temps réel, comme les jeux vidéo et la réalité virtuelle, se basent sur des techniques d’approximation et de précalcul pour atteindre des résultats réalistes. L’objectif de ce mémoire est l’investigation de différentes paramétrisations animées pour concevoir une technique d’approximation de rendu réaliste en temps réel. Notre investigation se concentre sur le rendu d’effets visuels appliqués à des personnages animés par modèle d’armature squelettique. Des paramétrisations combinant des données de mouvement et d’apparence nous permettent l’extraction de paramètres pour le processus en temps réel. Établir une dépendance linéaire entre le mouvement et l’apparence est ainsi au coeur de notre méthode. Nous nous concentrons sur l’occultation ambiante, où la simulation de l’occultation est causée par des objets à proximité bloquant la lumière environnante, jugée uniforme. L’occultation ambiante est une technique indépendante du point de vue, et elle est désormais essentielle pour le réalisme en temps réel. Nous examinons plusieurs paramétrisations qui traitent l’espace du maillage en fonction de l’information d’animation par squelette et/ou du maillage géométrique. Nous sommes capables d’approximer la réalité pour l’occultation ambiante avec une faible erreur. Notre technique pourrait également être étendue à d’autres effets visuels tels le rendu de la peau humaine (diffusion sous-surface), les changements de couleur dépendant du point de vue, les déformations musculaires, la fourrure ou encore les vêtements.While realistic rendering gains more popularity in industry, photorealistic and physically- based techniques often necessitate offline processing due to their computational complexity. Real-time applications, such as video games and virtual reality, rely mostly on approximation and precomputation techniques to achieve realistic results. The objective of this thesis is to investigate different animated parameterizations in order to devise a technique that can approximate realistic rendering results in real time. Our investigation focuses on rendering visual effects applied to skinned skeletonbased characters. Combined parameterizations of motion and appearance data are used to extract parameters that can be used in a real-time approximation. Trying to establish a linear dependency between motion and appearance is the basis of our method. We focus on ambient occlusion, a simulation of shadowing caused by objects that block ambient light. Ambient occlusion is a view-independent technique important for realism. We consider different parameterization techniques that treat the mesh space depending on skeletal animation information and/or mesh geometry. We are able to approximate ground-truth ambient occlusion with low error. Our technique can also be extended to different visual effects, such as rendering human skin (subsurface scattering), changes in color due to the view orientation, deformation of muscles, fur, or clothe

    Markerless Motion Capture in the Crowd

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    This work uses crowdsourcing to obtain motion capture data from video recordings. The data is obtained by information workers who click repeatedly to indicate body configurations in the frames of a video, resulting in a model of 2D structure over time. We discuss techniques to optimize the tracking task and strategies for maximizing accuracy and efficiency. We show visualizations of a variety of motions captured with our pipeline then apply reconstruction techniques to derive 3D structure.Comment: Presented at Collective Intelligence conference, 2012 (arXiv:1204.2991

    Intelligent visual media processing: when graphics meets vision

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    The computer graphics and computer vision communities have been working closely together in recent years, and a variety of algorithms and applications have been developed to analyze and manipulate the visual media around us. There are three major driving forces behind this phenomenon: i) the availability of big data from the Internet has created a demand for dealing with the ever increasing, vast amount of resources; ii) powerful processing tools, such as deep neural networks, provide e�ective ways for learning how to deal with heterogeneous visual data; iii) new data capture devices, such as the Kinect, bridge between algorithms for 2D image understanding and 3D model analysis. These driving forces have emerged only recently, and we believe that the computer graphics and computer vision communities are still in the beginning of their honeymoon phase. In this work we survey recent research on how computer vision techniques bene�t computer graphics techniques and vice versa, and cover research on analysis, manipulation, synthesis, and interaction. We also discuss existing problems and suggest possible further research directions

    Towards precise completion of deformable shapes

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    According to Aristotle, “the whole is greater than the sum of its parts”. This statement was adopted to explain human perception by the Gestalt psychology school of thought in the twentieth century. Here, we claim that when observing a part of an object which was previously acquired as a whole, one could deal with both partial correspondence and shape completion in a holistic manner. More specifically, given the geometry of a full, articulated object in a given pose, as well as a partial scan of the same object in a different pose, we address the new problem of matching the part to the whole while simultaneously reconstructing the new pose from its partial observation. Our approach is data-driven and takes the form of a Siamese autoencoder without the requirement of a consistent vertex labeling at inference time; as such, it can be used on unorganized point clouds as well as on triangle meshes. We demonstrate the practical effectiveness of our model in the applications of single-view deformable shape completion and dense shape correspondence, both on synthetic and real-world geometric data, where we outperform prior work by a large margin
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