Geometry images resample meshes to represent them as texture for efficient GPU processing by forcing a regular parameterization that often incurs a large amount of distortion. Previous approaches broke the geometry image into multiple rectangular or irregular charts to reduce distortion, but complicated the automatic level of detail one gets from MIP-maps of the geometry image. We introduce triangular-chart geometry images and show this new approach better supports the GPU-side representation and display of skinned dynamic meshes, with support for feature preservation, bounding volumes, and view-dependent level of detail. Triangular charts pack efficiently, simplify the elimination of T-junctions, arise naturally from an edge-collapse simplification base mesh, and layout more flexibly to allow their edges to follow curvilinear mesh features. To support the construction and application of triangular-chart geometry images, this article introduces a new spectral clustering method for feature detection, and new methods for incorporating skinning weights and skinned bounding boxes into the representation. This results in a tenfold improvement in fidelity when compared to quad-chart geometry images.

Achieving interactive performance in cloth animation has significant implications in computer games and other interactive graphics applications. Although much progress has been made, it is still much desired to have real-time high-quality results that well preserve dynamic folds and wrinkles. In this paper, we introduce a hybrid method for real-time cloth animation. It relies on data-driven models to capture the relationship between cloth deformations at two resolutions. Such data-driven models are responsible for transforming low-quality simulated deformations at the low resolution into high-resolution cloth deformations with dynamically introduced fine details. Our data-driven transformation is trained using rotation invariant quantities extracted from the cloth models, and is independent of the simulation technique chosen for the lower resolution model. We have also developed a fast collision detection and handling scheme based on dynamically transformed bounding volumes. All the components in our algorithm can be efficiently implemented on programmable graphics hardware to achieve an overall real-time performance on high-resolution cloth models.

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.

Skinned Mesh Animation (SMA) well approximates a mesh animation with extracted bones and their transformations. However, unlike skeleton, bones in SMA are not organized in hierarchies, thus they need mesh dependent translation vectors which prevent other sources of motion (i.e. skeletal animations, MoCAP, SMAs etc) from being applied to the skinned mesh. In this paper, we propose a new and fast method to transplant motion to skinned meshes. By efficiently solving a linear least-squares system, we can compute new translation vectors which enable the motion to work on the skinned mesh. Based on the same idea, we have also devised a SMA editing tool which allows users to edit frames of the SMA interactively. Furthermore, the editing can be propagated to all subsequent frames.

In this paper, we present a multigrid technique for efficiently deforming large surface and volume meshes. We show that a previous least-squares formulation for distortion minimization reduces to a Laplacian system on a general graph structure for which we derive an analytic expression. We then describe an efficient multigrid algorithm for solving the relevant equations. Here we develop novel prolongation and restriction operators used in the multigrid cycles. Combined with a simple but effective graph coarsening strategy, our algorithm can outperform other multigrid solvers and the factorization stage of direct solvers in both time and memory costs for large meshes. It is demonstrated that our solver can trade off accuracy for speed to achieve greater interactivity, which is attractive for manipulating large meshes. Our multigrid solver is particularly well suited for a mesh editing environment which does not permit extensive precomputation. Experimental evidence of these advantages is provided on a number of meshes with a wide range of size. With our mesh deformation solver, we also successfully demonstrate that visually appealing mesh animations can be generated from both motion capture data and a single base mesh even when they are inconsistent.