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Technical Papers
Motion Capture and Synthesis
Saturday, 01 December 09:00 - 10:45 | Peridot 206
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Lightweight Binocular Facial Performance Capture under Uncontrolled LightingWe propose a passive facial performance capture approach that reconstructs detailed dynamic facial geometry from a single stereo pair of cameras and succeeds under uncontrolled and time-varying lighting. Our method brings facial performance capture out of the studio, into the wild, and within the reach of everybody. Levi Valgaerts, Max Planck Institute for informatics |
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Accurate Realtime Full-body Performance Capture Using A Single Depth CameraWe present a new method for accurately capturing full-body performance using a single depth camera. Our system is robust, automatic, runs in real time, and allows for accurate reconstruction of human-body poses even under significant occlusions. We achieve state of the art accuracy in our comparison with alternatives. Xiaolin Wei, Texas A&M University |
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Data-driven Finger Motion Synthesis for Gesturing CharactersCreating compelling finger motions is a challenging and time-consuming process. Our method automatically adds detailed finger movements to the body motions of gesturing and conversing characters. We locate suitable finger motion segments from a database based on similarity of the arm motions and smoothness of the reconstructed finger motions. Sophie Jörg, Carnegie Mellon University, Clemson University |
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A Statistical Similarity Measure for Aggregate Crowd DynamicsWe present an information-theoretic method to measure similarity between observed, real-world data and visual simulation of aggregate motions for a complex system consisting of many individual agents. The resulting metric is robust to data noise, motion uncertainty, and correlates strongly with user perceptions of motion similarity. Stephen Guy, University of North Carolina - Chapel Hill |
Sponsored by ACMSIGGRAPH