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Abstract We extend approaches for skinning characters to the general setting of skinning deformable mesh animations. We provide an automatic algorithm for generating progressive skinning approximations, that is particularly efficient for pseudo-articulated motions. Our contributions include the use of nonparametric mean shift clustering of high-dimensional mesh rotation sequences to automatically identify statistically relevant bones, and robust least squares methods to determine bone transformations, bone-vertex influence sets, and vertex weight values. We use a low-rank data reduction model defined in the undeformed mesh configuration to provide progressive convergence with a fixed number of bones. We show that the resulting skinned animations enable efficient hardware rendering, rest pose editing, and deformable collision detection. Finally, we present numerous examples where skins were automatically generated using a single set of parameter values.

We present an algorithm that generates natural and intuitive deformations via direct manipulation for a wide range of shape representations and editing scenarios. Our method builds a space deformation represented by a collection of affine transformations organized in a graph structure. One transformation is associated with each graph node and applies a deformation to the nearby space. Positional constraints are specified on the points of an embedded object. As the user manipulates the constraints, a nonlinear minimization problem is solved to find optimal values for the affine transformations. Feature preservation is encoded directly in the objective function by measuring the deviation of each transformation from a true rotation. This algorithm addresses the problem of “embedded deformation ” since it deforms space through direct manipulation of objects embedded within it, while preserving the embedded objects ’ features. We demonstrate our method by editing meshes, polygon soups, mesh animations, and animated particle systems.

During dynamic activities, the surface of the human body moves in many subtle but visually significant ways: bending, bulging, jiggling, and stretching. We present a technique for capturing and animating those motions using a commercial motion capture system and approximately 350 markers. Although the number of markers is significantly larger than that used in conventional motion capture, it is only a sparse representation of the true shape of the body. We supplement this sparse sample with a detailed, actor-specific surface model. The motion of the skin can then be computed by segmenting the markers into the motion of a set of rigid parts and a residual deformation (approximated first as a quadratic transformation and then with radial basis functions). We demonstrate the power of this approach by capturing flexing muscles, high frequency motions, and abrupt decelerations on several actors. We compare these results both to conventional motion capture and skinning and to synchronized video of the actors.

Enveloping (or skinning) is the process that relates a skeleton, which an animator controls, to a 3-D surface mesh, which the audience sees. This process is necessary in most com-puter graphics applications that involve animated characters. The complexity (and speed) of enveloping solutions vary from photo-realistic muscle simulations used for movie pro-duction, to artifact-ridden heuristics such as linear blend skinning used for video games and training simulations. We propose a method for example-based enveloping of 3-D characters. We can ap-proximate the output of muscle simulations or other high-quality enveloping tools with a model that can be evaluated at speeds comparable to the fastest enveloping techniques. Our technique introduces a rotational regression model that can accurately capture common skinning behaviors such as muscle bulging, twisting, and challenging areas such as the shoulders. Our better treatment of rotational quantities is made possible by a framework that predicts mesh deformation gradients instead of mesh vertex positions. We reconstruct the vertex positions from deformation gradients in an additional step by solving a Poisson

Skinning of skeletally deformable models is extensively used for real-time animation of characters, creatures and similar objects. The standard solution, linear blend skinning, has some serious drawbacks that require artist intervention. Therefore, a number of alternatives have been proposed in recent years. All of them successfully combat some of the artifacts, but none challenge the simplicity and efficiency of linear blend skinning. As a result, linear blend skinning is still the number one choice for the majority of developers. In this paper, we present a novel skinning algorithm based on linear combination of dual quaternions. Even though our proposed method is approximate, it does not exhibit any of the artifacts inherent in previous methods and still permits an efficient GPU implementation. Upgrading an existing animation system from linear to dual quaternion skinning is very easy and has a relatively minor impact on run-time performance.

We present a method for extracting a hierarchical, rigid skeleton from a set of example poses. We then use this skeleton to not only reproduce the example poses, but create new deformations in the same style as the examples. Since rigid skeletons are used by most 3D modeling software, this skeleton and the corresponding vertex weights can be inserted directly into existing production pipelines. To create the skeleton, we first estimate the rigid transformations of the bones using a fast, face clustering approach. We present an efficient method for clustering by providing a Rigid Error Function that finds the best rigid transformation from a set of points in a robust, space efficient manner and supports fast clustering operations. Next, we solve for the vertex weights and enforce locality in the resulting weight distributions. Finally, we use these weights to determine the connectivity and joint locations of the skeleton.

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Abstract- The main challenge of tracking articulated structures like hands is their large number of degrees of freedom (DOFs). A realistic 3D model of the human hand has at least 26 DOFs. The arsenal of tracking approaches that can track such structures fast and reliably is still very small. This paper proposes a tracker based on ‘Stochastic Meta-Descent ’ (SMD) for optimizations in such highdimensional state spaces. This new algorithm is based on a gradient descent approach with adaptive and parameter-specific step sizes. The SMD tracker facilitates the integration of constraints, and combined with a stochastic sampling technique, can get out of spurious local minima. Furthermore, the integration of a deformable hand model based on linear blend skinning and anthropometrical measurements reinforce the robustness of our tracker. Experiments show the efficiency of the SMD algorithm in comparison with common optimization methods. 1

Achieving intuitive control of animated surface deformation while observing a specific style is an important but challenging task in computer graphics. Solutions to this task can find many applications in data-driven skin animation, computer puppetry, and computer games. In this paper, we present an intuitive and powerful animation interface to simultaneously control the deformation of a large number of local regions on a deformable surface with a minimal number of control points. Our method learns suitable deformation subspaces from training examples, and generate new deformations on the fly according to the movements of the control points. Our contributions include a novel deformation regression method based on kernel Canonical Correlation Analysis (CCA) and a Poisson-based translation solving technique for easy and fast deformation control based on examples. Our run-time algorithm can be implemented on GPUs and can achieve a few hundred frames per second even for large datasets with hundreds of training examples.