: Motion style synthesis and retargeting: (top) after observing an unknown actor performing one walking style, we can synthesize other walking styles for the same actor; (bottom) we can transfer the walking style from one actor to another. This paper presents a generative human motion model for synthesis, retargeting, and editing of personalized human motion styles. We first record a human motion database from multiple actors performing a wide variety of motion styles for particular actions. We then apply multilinear analysis techniques to construct a generative motion model of the form x = g(a, e) for particular human actions, where the parameters a and e control "identity" and "style" variations of the motion x respectively. The new modular representation naturally supports motion generalization to new actors and/or styles. We demonstrate the power and flexibility of the multilinear motion models by synthesizing personalized stylistic human motion and transferring the stylistic motions from one actor to another. We also show the effectiveness of our model by editing stylistic motion in style and/or identity space.
Min et al. REF present a generative human motion model for synthesis of personalized human motion styles by constructing a multilinear motion model that provides explicit parametrized representation of human motion in terms of "style" and "identity" factors.
Synthesis and editing of personalized stylistic human motion
I3D '10
Computer Science