A Deformation Transformer for Real-Time Cloth Animation

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 datadriven 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.

Feature-Preserving Triangular Geometry Images for Level-of-Detail Representation of Static and Skinned Meshes

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 paper 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 ten-fold improvement in fidelity when compared to quadchart geometry images.

Robust Feature-Preserving Mesh Denoising Based on Consistent Sub-Neighborhoods

In this paper, we introduce a feature-preserving denoising algorithm. It is built on the premise that the underlying surface of a noisy mesh is piecewise smooth, and a sharp feature lies on the intersection of multiple smooth surface regions. A vertex close to a sharp feature is likely to have a neighborhood that includes distinct smooth segments. By defining the consistent sub-neighborhood as the segment whose geometry and normal orientation most consistent with those of the vertex, we can completely remove the influence from neighbors lying on other segments during denoising. Our method identifies piecewise smooth sub-neighborhoods using a robust density-based clustering algorithm based on shared nearest neighbors.

In our method, we obtain an initial estimate of vertex normals and curvature tensors by robustly fitting a local quadric model. An anisotropic filter based on optimal estimation theory is further applied to smooth the normal field and the curvature tensor field. This is followed by second-order bilateral filtering, which better preserves curvature details and alleviates volume shrinkage during denoising. The support of these filters is defined by the consistent sub-neighborhood of a vertex. We have applied this algorithm to both generic and CAD models, and sharp features, such as edges and corners, are very well preserved.i

Real-Time Data Driven Deformation with Affine Bones

Data driven deformation is increasingly important in computer graphics and interactive applications. From given mesh example sequences, we train a deformation predictor and manipulate a specific style of surface deformation interactively using only a small number of control points. The latest approach of learning the connection between rigid bone transformations and control points uses a statistically based framework, called canonical correlation analysis. In this paper, we extend this approach to a skinned mesh with affine bones, each of which conveys a nonrigid affine transformation. However, it is difficult to discover the underlying relationship between control points and nonrigid transformations. To address this issue, we present a two-layer regression framework; one layer being from control points to rigid and the other layer being from rigid to nonrigid transformations. Our contributions also include bone-vertex weight smoothing, enabling the distribution of each bone's influence across neighboring vertices. We can alleviate distortion around regions where nearby bones undergo various transformations and improve deformations reaching beyond the learned subspaces. Experimental results show that our method can achieve more general deformations including flexible muscle bulges or twists. The performance of our implementation is comparable to the latest approach.

Real-Time Data-Driven Deformation Using Kernel Canonical Correlation Analysis

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.

Gradient Domain Editing of Deforming Mesh Sequences

Many graphics applications, including computer games and 3D animated films, make heavy use of deforming mesh sequences. In this paper, we generalize gradient domain editing to deforming mesh sequences. Our framework is keyframe based. Given sparse and irregularly distributed constraints at unevenly spaced keyframes, our solution first adjusts the meshes at the keyframes to satisfy these constraints, and then smoothly propagate the constraints and deformations at keyframes to the whole sequence to generate new deforming mesh sequence. To achieve convenient keyframe editing, we have developed an efficient alternating least-squares method. It harnesses the power of subspace deformation and two-pass linear methods to achieve high-quality deformations. We have also developed an effective algorithm to define boundary conditions for all frames using handle trajectory editing. Our deforming mesh editing framework has been successfully applied to a number of editing scenarios with increasing complexity, including footprint editing, path editing, temporal filtering, handle-based deformation mixing, and spacetime morphing.

A Fast Multigrid Algorithm for Mesh Deformation

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.

Mesh Editing with Poisson-Based Gradient Field Manipulation

In this paper, we introduce a novel approach to mesh editing with the Poisson equation as the theoretical foundation. The most distinctive feature of this approach is that it modifies the original mesh geometry implicitly through gradient field manipulation. Our approach can produce desirable and pleasing results for both global and local editing operations, such as deformation, object merging, and smoothing. With the help from a few novel interactive tools, these operations can be performed conveniently with a small amount of user interaction. Our technique has three key components, a basic mesh solver based on the Poisson equation, a gradient field manipulation scheme using local transforms, and a generalized boundary condition representation based on local frames. Experimental results indicate that our framework can outperform previous related mesh editing techniques.

Extracting Objects from Range and Radiance Images

In this project, we developed two key techniques necessary for editing a real scene captured with both cameras and laser range scanners. We develop automatic algorithms to segment the geometry from range images into distinct surfaces, and register texture from radiance images with the geometry. The result is an object-level representation of the scene which can be rendered with modifications to structure via traditional rendering methods.

The algorithms have been applied to large-scale real data. We can reconstruct meshes and texture maps for individual objects and render them realistically. We demonstrate our ability to edit a captured scene by moving, inserting, and deleting objects.

Surface Reconstruction from Unorganized Points Using Neural Networks

We introduce a novel technique for surface reconstruction from unorganized points by applying Kohonen's self-organizing map. The topology of the surface is predetermined, and a neural network learning algorithm is carried out to obtain correct 3D coordinates at each vertex of the surface. Edge swap and multiresolution learning are proposed to make the algorithm more effective and more efficient. The whole algorithm is very simple to implement. Experimental results have shown our techniques are successful.