Abstract: Computer animations and virtual environments both require a controllable source of motion for their characters. Most of the currently available technologies require significant training and are not useful tools for naive users. Over the past few years, we have explored several different solutions to this problem. Each solution relies on the information about natural human motion inherent in a motion capture database. For example, the user can sketch an approximate path for an animated character which is then refined by searching a motion graph. We can also find a natural looking motion for a particular behavior based on sparse constraints from the user (foot contact locations and timing, for example) by optimizing in a low-dimensional, behavior-specific space found from motion capture. Motion capture data can also be used to bias planning algorithms toward more natural postures for constrained manipulation tasks such as placing a large box into a car trunk. And finally, we have developed a performance animation system that uses video input of the user to control a swing dancing couple. The system finds the pose of the user with machine learning techniques trained using motion capture data.

Short Bio: Jessica Hodgins is a Professor in the Robotics Institute and Computer Science Department at Carnegie Mellon University. Prior to moving to CMU in 2000, she was an an Associate Professor and Assistant Dean in the College of Computing at Georgia Institute of Technology. She received her Ph.D. in Computer Science from Carnegie Mellon University in 1989. Her research focuses on computer graphics, animation, and robotics. She has received a NSF Young Investigator Award, a Packard Fellowship, and a Sloan Fellowship. She was editor-in-chief of ACM Transactions on Graphics from 2000-2002 and SIGGRAPH Papers Chair in 2003.