High Resolution Registration, Tracking, and Synthesis of 3D Deforming Objects

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Vision Journal Club, Nov 02, 2012, 02:00PM – 03:00PM, Wachman 447

High Resolution Registration, Tracking, and Synthesis of 3D Deforming Objects

Dr. Yang Wang, Siemens Corporate Research

Abstract: Recent advent of new technologies allows us to capture massive amounts of high resolution, high frame rate geometry and appearance data in 3D. In order to use such data for the temporal analysis and realistic synthesis of dynamic motion, an efficient object registration and tracking algorithm is needed to establish dense correspondences. This problem remains challenging for non-rigid objects (e.g., faces and hearts) especially when subtle motion estimation is required. In this talk, I will present manifold-based approaches for deformable surface registration and object tracking. An intrinsic shape representation is introduced in the first part for 3D non-rigid surface matching. Furthermore, a low-dimensional representation is learned to encode the non-rigid motion prior based on manifold learning. Due to the strong implicit and explicit smoothness constraints imposed by the algorithm and the high-resolution data, we can establish dense inter-frame correspondences for analyzing and synthesizing subtle non-rigid motion. Several graphics and medical applications are included to demonstrate the proposed approaches.
Dr. Yang Wang is a research scientist in Siemens Corporate Research, located at Princeton, NJ. Prior to SCR, he worked as a post-doctoral research fellow in Robotics Institute at CMU from 2006 to 2008. He received his Ph.D. degree from Stony Brook University in 2006. Dr. Wang specializes in non-rigid shape matching and motion tracking, medical image analysis, face recognition, and facial expression analysis. He has more than 35 published papers and 5 patent applications in the above areas. Dr. Wang is a member of ACM and IEEE.