Learning-Based Sphere Nonlinear Interpolation for Motion Synthesis

Motion synthesis technology can produce natural and coordinated motion data without a motion capture process, which is complex and costly. Current motion synthesis methods usually provide a few interfaces to avoid the arbitrariness of the synthesis process, but this actually reduces the understandability of the synthesis process. In this paper, we propose a learning-based Sphere nonlinear interpolation (Snerp) model that can generate natural in-between motions in terms of a given start-end frame pair. Variety of the input frame pairs will enrich the diversity of the generated motions. The angle speed of natural human motion is not uniform and presents different change rules (we call them motion patterns) for different motions, so we first extract the motion patterns and then build the relation between motion pattern space and frame pair space via a paired dictionary learning process. After learning, we estimate the motion pattern according to the representation of a given start-end frame pair on the frame pair dictionary. We select several different types of start-end frame pairs from the real motion sequences as the testing data and good results of both objective and subjective evaluations on the generated motions demonstrate the superior performance of Snerp.