The human body is complex and deformable. For many applications in computer vision, graphics, fashion, and medicine, having a realistic, low-dimensional, 3D model of the body is useful. Getting a good one, however, is difficult. This talk will review different shape representations and how to learn 3D models of the human body from 3D scans. It will try to answer "what" is a body model, "why" it is useful, and "how" to build one. It will summarize how to accurately align 3D meshes of bodies in arbitrary poses, how to build a statistical model of body shape and non-rigid pose variation, and how to fit such models to data including 3D scans, Kinect data, or mocap markers. The talk will also describe work on capturing and modeling the dynamics of soft tissue motion using our one-of-a-kind 4D body scanner.
Powerpoint slides: http://files.is.tue.mpg.de/black/talks/BodiesIPAM2016web.pptx
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