Physically-based Animation Rendering with Markov Chain Monte Carlo

Abstract

Exploring temporal coherence among light transport paths is very important to remove temporally perception-sensitive artifacts in animation rendering. Using the contribution of a light transport path to all frames in an animation as the sampling distribution function allows us to adapt Markov Chain Monte Carlo (MCMC) algorithms to exploit the temporal and spatial coherence among paths in order to generate a perceptually pleasant animation. A new perturbation technique called time perturbation is developed to explore the temporal coherence among paths. Furthermore, in order to make animation rendering plausible, we distribute iterative computational tasks to a pool of computers for parallel computation. Each task is rendered with a set of parameters adapted according to the local properties of each task. We demonstrate that this local adaptation does not introduce bias statistically. The resulting animations are perceptually better than those rendered in a frame-by-frame manner.