Sparse Constrained Motion Synthesis Using Local Regression Models

Based on sparse control constraints, one diffi- culty for synthesizing natural human motions is that low- dimensional control information can not be directly used to construct high-dimensional human poses. This paper intro- duces a novel and powerful local dimensionality reduction approach for synthesizing accurate and natural full-body human motions. The approach is to construct a group of online local dynamic regression models from a pre-captured motion database as a prior to support the full-body human action synthesis. By synthesizing a variety of human motions from as possible as few sparse constraints provided by users, the paper verifies the effectiveness of the proposed approach. Compared with previous statistical models, our model can synthesize more accurate results.