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Techniques for removing and synthesizing secondary dynamics in facial performance capture
Techniques for removing and synthesizing secondary dynamics in facial performance capture
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机译：在面部性能捕获中去除和合成二级动力学的技术
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摘要
A removal model is trained to predict secondary dynamics associated with an individual enacting a performance. For a given sequence of frames that includes an individual enacting a performance and secondary dynamics, a retargeting application identifies a set of rigid points that correspond to skeletal regions of the individual and a set of non-rigid points that correspond to non-skeletal region of the individual. For each frame in the sequence of frames, the application applies the removal model that takes as inputs a velocity history of a non-rigid point and a velocity history of the rigid points in a temporal window around the frame, and outputs a delta vector for the non-rigid point indicating a displacement for reducing secondary dynamics in the frame. In addition, a trained synthesis model can be applied to determine a delta vector for every non-rigid point indicating displacements for adding new secondary dynamics.
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