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Liang, Ziheng

University of British Columbia

Real-time animations are limited by a computational budget and often trade realism for performance. Simulating the shape of a realistic human body requires a tremendous amount of computational resources and may be infeasible in real-time applications. In recent years, deep learning approaches have proven their effectiveness in fields such as computer vision. We present a new method that combines ideas from deep learning and example-based skinning. The method approximates corrections to skin deformation from a skeleton-based animation baseline. A key aspect of the approach is to factor the network into two parts, with part of the network evaluated using shaders in the standard real-time graphics rendering pipeline. Our method adds a minimum overhead to a skeleton-based animation while improving its visual results.

Text

2023-04-30

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/81335

Computer Science

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/