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SoftDECA: Computationally Efficient Physics-Based Facial Animations
Computationally efficient physics-based facial animation method integrating soft tissue dynamics with the DECA morphable face model.
Abstract
Facial animation on computationally weak systems is still mostly dependent on linear blendshape models. However, these models suffer from typical artifacts such as loss of volume, self-collisions, or erroneous soft tissue elasticity. In addition, while extensive effort is required to personalize blendshapes, there are limited options to simulate or manipulate physical and anatomical properties once a model has been crafted. Finally, second-order dynamics can only be represented to a limited extent. For decades, physics-based facial animation has been investigated as an alternative to linear blendshapes but is still cumbersome to deploy and results in high computational cost at runtime. We propose SoftDECA, an approach that provides the benefits of physics-based simulation while being as effortless and fast to use as linear blendshapes. SoftDECA is a novel hypernetwork that efficiently approximates a FEM-based facial simulation while generalizing over the comprehensive DECA model of human identities, facial expressions, and a wide range of material properties that can be locally adjusted without re-training. Along with SoftDECA, we introduce a pipeline for creating the needed high-resolution training data. Part of this pipeline is a novel layered head model that densely positions the biomechanical anatomy within a skin surface while avoiding self-intersections.
How to read this
- Category
- Method: efficient physics-based facial animation
- Contributions
- A hypernetwork (SoftDECA) that approximates FEM-based facial simulation at speeds comparable to linear blendshapes
- Generalization over the DECA model of identities, expressions, and a range of materials, with locally adjustable properties without retraining
- A pipeline for creating the high-resolution data the approach needs
- Context
- Builds on the DECA morphable face model (Feng et al.) to bring physics-based soft-tissue behavior to blendshape-speed runtimes.Builds on: Learning an Animatable Detailed 3D Face Model from In-The-Wild Images
- Correctness
- It assumes a learned hypernetwork can faithfully stand in for FEM simulation across identities and materials; presented as addressing blendshape artifacts (volume loss, self-collision, limited dynamics), though fidelity to true FEM and behavior outside the training distribution warrant scrutiny.
- Clarity
- Moderately technical; a first pass conveys the blendshape-versus-physics motivation and the hypernetwork idea, a second pass clarifies the FEM approximation and data pipeline.
- How to read it
- Focus on what the hypernetwork approximates and which blendshape artifacts it removes; a second pass on the FEM setup and material parameterization pays off if you build facial rigs.
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