Intuitive Interactive Human-Character Posing with Millions of Example Poses

We present a data-driven algorithm for interactive 3D human character posing. We formulate the problem in a maximum a posteriori (MAP) framework by combining the user’s inputs with the priors embedded in prerecorded human poses. Maximizing the posteriori allows us to generate a most likely human pose that satisfies the user constraints. One unique property of our system is its ability to learn priors from a huge and heterogeneous human motion capture database (2.8 million prerecorded poses) and use them to generate a wide range of natural poses, a capacity that has not been demonstrated in previous data-driven character posing systems. In addition, we present two intuitive interfaces for interactive human character posing: direct manipulation interfaces and sketching interfaces. We show the superiority of our system by comparing it with standard inverse kinematics techniques as well as to alternative data-driven techniques.

• Type: Journal
• Year: 2011
• Where: CGA
• Authors: Xiaolin K. Wei, Jinxiang Chai
• Keywords: CGA 2011, Computational Geometry, Human Motion Capture, Intuitive Interfaces, Manipulation Interfaces