Spatio-temporal reconstruction for 3D motion recovery

—This paper addresses the challenge of 3D motion recovery by exploiting the spatio-temporal correlations of corrupted 3D skeleton sequences. We propose a new 3D motion recovery method using spatio-temporal reconstruction, which uses joint low-rank and sparse priors to exploit temporal correlation and an isometric constraint for spatial correlation. The proposed model is formulated as a constrained optimization problem, which is efficiently solved by the augmented Lagrangian method with a Gauss-Newton solver for the subproblem of isometric optimization. Experimental results on the CMU motion capture dataset, Edinburgh dataset and two Kinect datasets demonstrate that the proposed approach achieves better motion recovery than state-of-the-art methods. The proposed method is applicable to Kinect-like skeleton tracking devices and pose estimation methods that cannot provide accurate estimation of complex motions, especially in the presence of occlusion

Automatic skeletonization and skin attachment for realistic character animation.

The realism of character animation is associated with a number of tasks ranging from modelling, skin defonnation, motion generation to rendering. In this research we are concerned with two of them: skeletonization and weight assignment for skin deformation. The fonner is to generate a skeleton, which is placed within the character model and links the motion data to the skin shape of the character. The latter assists the modelling of realistic skin shape when a character is in motion. In the current animation production practice, the task of skeletonization is primarily undertaken by hand, i.e. the animator produces an appropriate skeleton and binds it with the skin model of a character. This is inevitably very time-consuming and costs a lot of labour. In order to improve this issue, in this thesis we present an automatic skeletonization framework. It aims at producing high-quality animatible skeletons without heavy human involvement while allowing the animator to maintain the overall control of the process. In the literature, the tenn skeletonization can have different meanings. Most existing research on skeletonization is in the remit of CAD (Computer Aided Design). Although existing research is of significant reference value to animation, their downside is the skeleton generated is either not appropriate for the particular needs of animation, or the methods are computationally expensive. Although some purpose-build animation skeleton generation techniques exist, unfortunately they rely on complicated post-processing procedures, such as thinning and pruning, which again can be undesirable. The proposed skeletonization framework makes use of a new geometric entity known as the 3D silhouette that is an ordinary silhouette with its depth information recorded. We extract a curve skeleton from two 3D silhouettes of a character detected from its two perpendicular projections. The skeletal joints are identified by down sampling the curve skeleton, leading to the generation of the final animation skeleton. The efficiency and quality are major performance indicators in animation skeleton generation. Our framework achieves the former by providing a 2D solution to the 3D skeletonization problem. Reducing in dimensions brings much faster performances. Experiments and comparisons are carried out to demonstrate the computational simplicity. Its accuracy is also verified via these experiments and comparisons. To link a skeleton to the skin, accordingly we present a skin attachment framework aiming at automatic and reasonable weight distribution. It differs from the conventional algorithms in taking topological information into account during weight computation. An effective range is defined for a joint. Skin vertices located outside the effective range will not be affected by this joint. By this means, we provide a solution to remove the influence of a topologically distant, hence highly likely irrelevant joint on a vertex. A user-defined parameter is also provided in this algorithm, which allows different deformation effects to be obtained according to user's needs. Experiments and comparisons prove that the presented framework results in weight distribution of good quality. Thus it frees animators from tedious manual weight editing. Furthermore, it is flexible to be used with various deformation algorithms

Skin deformation and animation of character models based on static and dynamic ordinary differential equations.

Animated characters play an important role in the field of computer animation, simulation and games. The basic criterion of good character animation is that the animated characters should appear realistic. This can be achieve through proper skin deformations for characters. Although various skin deformation approaches (Joint-based, Example-based, Physics-based, Curve-based and PDE-based) have been developed, the problem of generating realistic skin deformations efficiently with a small data set is a big challenge. In order to address the limitations of skin deformation, this thesis presents a workflow consisting of three main steps. First, the research has developed a new statistical method to determine the positions of joints based on available X-ray images. Second, an effective method for transferring the deformations of the curves to the polygonal model with high accuracy has been developed. Lastly, the research has produced a simple and efficient method to animate skin deformations by introducing a curved-based surface manipulation method combined with physics and data-driven approaches. The novelty of this method depends on a new model of dynamic deformations and an efficient finite difference solution of the model. The application examples indicate that the curve-based dynamic method developed in this thesis can achieve good realism and high computational efficiency with small data sets in the creation of skin deformations