title: Real-time stylistic prediction for whole-body human motions.
abstract: The ability to predict human motion is crucial in several contexts such as human tracking by computer vision and the synthesis of human-like computer graphics. Previous work has focused on off-line processes with well-segmented data; however, many applications such as robotics require real-time control with efficient computation. In this paper, we propose a novel approach called real-time stylistic prediction for whole-body human motions to satisfy these requirements. This approach uses a novel generative model to represent a whole-body human motion including rhythmic motion (e.g., walking) and discrete motion (e.g., jumping). The generative model is composed of a low-dimensional state (phase) dynamics and a two-factor observation model, allowing it to capture the diversity of motion styles in humans. A real-time adaptation algorithm was derived to estimate both state variables and style parameter of the model from non-stationary unlabeled sequential observations. Moreover, with a simple modification, the algorithm allows real-time adaptation even from incomplete (partial) observations. Based on the estimated state and style, a future motion sequence can be accurately predicted. In our implementation, it takes less than 15 ms for both adaptation and prediction at each observation. Our real-time stylistic prediction was evaluated for human walking, running, and jumping behaviors.
authors.affiliation: Graduate School of Information Science, NAIST, 8916-5, Takayama-cho, Ikoma, Nara, 630-0101, Japan. takam-m@is.naist.jp
authors.forename: Takamitsu
authors.initials: T
authors.lastname: Matsubara
authors.forename: Sang-Ho
authors.initials: SH
authors.lastname: Hyon
authors.forename: Jun
authors.initials: J
authors.lastname: Morimoto
mesh_terms: D000222:Adaptation, Physiological / Q000502:physiology; D003199:Computer Systems / Q000706:statistics & numerical data; D005544:Forecasting; D006801:Humans; D009038:Motion; D016138:Walking / Q000502:physiology