Near-realtime Facial Animation by Deep 3D Simulation Super-Resolution
- URL: http://arxiv.org/abs/2305.03216v2
- Date: Thu, 10 Aug 2023 01:59:55 GMT
- Title: Near-realtime Facial Animation by Deep 3D Simulation Super-Resolution
- Authors: Hyojoon Park, Sangeetha Grama Srinivasan, Matthew Cong, Doyub Kim,
Byungsoo Kim, Jonathan Swartz, Ken Museth, Eftychios Sifakis
- Abstract summary: We present a neural network-based simulation framework that can efficiently and realistically enhance a facial performance produced by a low-cost, realtime physics-based simulation.
We use face animation as an exemplar of such a simulation domain, where creating this semantic congruence is achieved by simply dialing in the same muscle actuation controls and skeletal pose in the two simulators.
Our proposed neural network super-resolution framework generalizes from this training set to unseen expressions, compensates for modeling discrepancies between the two simulations due to limited resolution or cost-cutting approximations in the real-time variant, and does not require any semantic descriptors or parameters to
- Score: 7.14576106770047
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We present a neural network-based simulation super-resolution framework that
can efficiently and realistically enhance a facial performance produced by a
low-cost, realtime physics-based simulation to a level of detail that closely
approximates that of a reference-quality off-line simulator with much higher
resolution (26x element count in our examples) and accurate physical modeling.
Our approach is rooted in our ability to construct - via simulation - a
training set of paired frames, from the low- and high-resolution simulators
respectively, that are in semantic correspondence with each other. We use face
animation as an exemplar of such a simulation domain, where creating this
semantic congruence is achieved by simply dialing in the same muscle actuation
controls and skeletal pose in the two simulators. Our proposed neural network
super-resolution framework generalizes from this training set to unseen
expressions, compensates for modeling discrepancies between the two simulations
due to limited resolution or cost-cutting approximations in the real-time
variant, and does not require any semantic descriptors or parameters to be
provided as input, other than the result of the real-time simulation. We
evaluate the efficacy of our pipeline on a variety of expressive performances
and provide comparisons and ablation experiments for plausible variations and
alternatives to our proposed scheme.
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