Truong of University of California San Diego

3D Dynamic Meshes can deliver engaging experiences in various applications, but the storage and transmission demands associated with these data structures can be prohibitive. We address this challenge with an efficient compression technique leveraging embedded key nodes. The temporal motion of each vertex is formulated as a distance-weighted combination of transformations from neighboring key nodes, requiring the transmission of solely the key nodes' transformations. Through extensive experiments, we demonstrate the effectiveness of our method in significantly reducing storage requirements while preserving the immersive quality of the visual content.

Biography:

Truong Nguyen received his B.S., M.S. and Ph.D. in Electrical Engineering at California Institute of Technology in 1985, 1986 and 1989. His current research areas include 3D Human Mesh segmentation and coding as well as machine learning for medical image analysis. During his academic career, he has published over 200 peer-reviewed journal papers, over 380 peer-reviewed conference papers, 1 textbook, 3 book chapters, and 15 issued patents. His Google H-index is 72 with over 31K citations. He received the NSF Career Award in 1995, and IEEE Signal Processing Society's 1992 Paper Award (student author) for the 1990 paper "Structures for M-Channel Perfect-Reconstruction FIR QMF Banks Which Yield Linear-Phase Analysis Filters" (IEEE Trans. ASSP). Не received the Distinguished Teaching Award at UCSD in 2019 and three teaching awards from the ECE Dept. at UCSD in 2006, 2008 and 2010. He is a Fellow of IEEE.