Social Interactive Human Video Synthesis

In this paper, we propose a computational model for social interaction between three people in a conversation, and demonstrate results using human video motion synthesis. We utilised semi-supervised computer vision techniques to label social signals between the people, like laughing, head nod and gaze direction. Data mining is used to deduce frequently occurring patterns of social signals between a speaker and a listener in both interested and not interested social scenarios, and the mined confidence values are used as conditional probabilities to animate social responses. The human video motion synthesis is done using an appearance model to learn a multivariate probability distribution, combined with a transition matrix to derive the likelihood of motion given a pose configuration. Our system uses social labels to more accurately define motion transitions and build a texture motion graph. Traditional motion synthesis algorithms are best suited to large human movements like walking and...

• Type: Journal
• Year: 2010
• Where: ACCV
• Authors: Dumebi Okwechime, Eng-Jon Ong, Andrew Gilbert, Richard Bowden
• Keywords: ACCV 2010, Computer Vision, Human Video Motion, Motion Synthesis, Social