Toward body language generation in dyadic interaction settings from interlocutor multimodal cues
During dyadic interactions, participants influence each other's verbal and nonverbal behaviors. In this paper, we examine the coordination between a dyad's body language behavior, such as body motion, posture and relative orientation, given the participants' communication goals, e.g., friendly or conflictive, in improvised interactions. We further describe a Gaussian Mixture Model (GMM) based statistical methodology for automatically generating body language of a listener from speech and gesture cues of a speaker. The experimental results show that automatically generated body language trajectories generally follow the trends of observed trajectories, especially for velocities of body and arms, and that the use of speech information improves prediction performance. These results suggest that there is a significant level of predictability of body language in the examined goal-driven improvisations, which could be exploited for interaction-driven and goal-driven body language generation.