Expressive gesture animation based on non parametric learning of sensory-motor models

This paper presents an efficient method of learning motion control for autonomous animated characters. The method uses a nonparametric learning approach which identifies nonlinear mappings between sensory signals and motor control. The learning phase is handled through a general regression neural network model simulated by using near neighbors search algorithms (kd-tree). The resulting adaptive model (ASMM) is suitable for the expressive animation of an anthropomorphic hand-arm system involved in reaching or tracking tasks.