Abstract Even with the advent of hardware‐accelerated ray tracing in modern GPUs, only a small number of rays can be traced at each pixel in real‐time applications. This presents a significant challenge for path tracing, even when augmented with state‐of‐the art denoising algorithms. While the recently‐developed ReSTIR algorithm [BWP∗20] enables high‐quality renderings of scenes with millions of light sources using just a few shadow rays at each pixel, there remains a need for effective algorithms to sample indirect illumination. We introduce an effective path sampling algorithm for indirect lighting that is suitable to highly parallel GPU architectures. Building on the screen‐space spatio‐temporal resampling principles of ReSTIR, our approach resamples multi‐bounce indirect lighting paths obtained by path tracing. Doing so allows sharing information about important paths that contribute to lighting both across time and pixels in the image. The resulting algorithm achieves a substantial error reduction compared to path tracing: at a single sample per pixel every frame, our algorithm achieves MSE improvements ranging from 9.3× to 166× in our test scenes. In conjunction with a denoiser, it leads to high‐quality path traced global illumination at real‐time frame rates on modern GPUs.

Abstract Rig‐space physics a finite element method(FEM) based simulation technique that aims at adding secondary motion on a character while maintaining seamless cooperation with traditional animation pipelines. We enhance the rig‐space physics by introducing several techniques, including general field interaction, proportional‐derivative control, and improved material control. This allows an animator to perform various interferences to the simulation process and create more abundant animation effects. Moreover, we also improve the numerical stability of the simulation algorithm by prepending a conjugate gradient procedure. Copyright © 2016 John Wiley & Sons, Ltd.

This paper proposes a framework for matting identification photos automatically. We firstly adopt image processing operations such as skin detection, k-means clustering, Grab cut segmentation and canny operator to generate a trimap with three regions, i.e., background, foreground and unknown regions. Then, we improve the Bayesian matting by introducing an alpha regularization term. Experiments demonstrate that our system can achieve about 86% acceptance rate for real photo datasets.

We present an automatic shape composition method to fuse two shape parts which may not overlap and possibly contain sharp features, a scenario often encountered when modeling man-made objects. At the core of our method is a novel field-guided approach to automatically align two input parts in a feature-conforming manner. The key to our field-guided shape registration is a natural continuation of one part into the ambient field as a means to introduce an overlap with the distant part, which then allows a surface-to-field registration. The ambient vector field we compute is feature-conforming; it characterizes a piecewise smooth field which respects and naturally extrapolates the surface features. Once the two parts are aligned, gap filling is carried out by spline interpolation between matching feature curves followed by piecewise smooth least-squares surface reconstruction. We apply our algorithm to obtain feature-conforming shape composition on a variety of models and demonstrate generality of the method with results on parts with or without overlap and with or without salient features.