Comic character animation using Bayesian estimation
The motion of comic characters includes different types of movements, such as walking or running. In a comic, a movement may be described by a series of non-continuous poses in a sequence of contiguous frames. Each pose exists in a frame. We synthesize an animation according to still comic frames. In this paper, we propose a model to analyze time series of a character's motion using the non-parametric Bayesian approach. Then we can automatically generate a sequence of motions by using the estimated time series. Experimental results show that the built time series model best matches the given frames. Furthermore, unnatural distortions of the results are minimized. Copyright (C) 2011 John Wiley & Sons, Ltd.
time series image deformation functional approximation bayesian inference elliptic radial basis functions locally weighted regression