Human motion simulation and action corpus
ICDHM'07 Proceedings of the 1st international conference on Digital human modeling
Simulation of human motion for learning and recognition
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
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Optimization techniques have proven to be a powerful approach for generating new motions. In this paper, we present a physically based optimization method to synthesize motions by using motion capture data as input. We assume that the captured motion data is physically plausible. We start by defining and estimating the physical properties of human characters. The procedure of motion synthesis is from coarse to fine according to the objective function and physical constraints. Our motion synthesis is like a motion editing method, which is appropriate for motion correction and extrapolation. By this means, we can correct and eliminate unrealistic motion data.