A Study of Synthesizing New Human Motions from Sampled Motions Using Tensor Decomposition

This paper applies an algorithm, based on Tensor Decomposition, to a new synthesis application: by using sampled motions of people of different ages under different emotional states, new motions for other people are synthesized. Human motion is the composite consequence of multiple elements, including the action performed and a motion signature that captures the distinctive pattern of movement of a particular individual. By performing decomposition, based on N-mode SVD (singular value decomposition), the algorithm analyzes motion data spanning multiple subjects performing different actions to extract these motion elements. The analysis yields a generative motion model that can synthesize new motions in the distinctive styles of these individuals. The effectiveness of applying the tensor decomposition approach to our purpose was confirmed by synthesizing novel walking motions for a person by using the extracted signature.

• Type: Conference
• Year: 2005
• Where: ICMCS
• Authors: Rovshan Kalanov, Jieun Cho, Jun Ohya
• Keywords: ICMCS 2005, Singular Value Decomposition, Tensor Decomposition, Tensor Decomposition Approach