Abstract:

Motion capture technologies have made revolutionary progress in computer animation in the past decade. With the detailed motion data and editing algorithms, we can directly transfer expressive performance of a real person to a virtual character, interpolate existing data to produce new sequences, or compose simple motion clips to create a rich repertoire of motor skills. In addition to computer graphics applications, motion capture technologies have enabled tremendous advancement in computer vision, robotics, biomechanics, and natural user interactions.

In this talk, I will describe our recent efforts on acquiring high-fidelity human motion. First, I will show how to capture physically realistic 3D full-body performances (e.g., gymnastics) from a normal 2D video taken with an ordinary video camera. This is the first video-based motion capture technology that simultaneously captures full-body poses, joint torques, and contact forces using single-camera video streams. In the second part of my talk, I will describe an approach for acquiring high-fidelity 3D facial performances such as large scale facial deformations and subtle wrinklings. This is the first 3D facial performance acquisition system that allows for capturing high-fidelity facial performances, which match both the spatial resolution of advanced 3D scanning technology and the acquisition speed of motion capture systems.

Presenter:  Jinxiang Chai

 

Bio:

Jinxiang Chai joined Texas A&M University in August 2006 as an assistant professor in Department of Computer Science and Engineering. He received his Ph.D in robotics from the School of Computer Science, Carnegie Mellon University in 2006. His primary research is in the area of computer graphics and animation with broad applications in other disciplines such as computer vision, robotics, human computer interaction, and biomechanics. He is particularly interested in developing representations and efficient computational models that allow acquisition, analysis, understanding, simulation, and control of natural human movements. He draws on ideas from graphics, vision, machine learning, robotics, biomechanics, psychology, and applied math. His research work has been published at the top venues of computer graphics and computer vision, including nine papers presented at SIGGRAPH, and has been cited more than 1900 times according to Google Scholar. He recently received an NSF CAREER award for his work on theory and practice of Bayesian human motion synthesis.