High-Quality Face Capture Using Anatomical Muscles
This work addresses the need for anatomically accurate and interpretable face capture for applications in computer graphics and vision, though it is incremental as it modifies an existing muscle-based system.
The paper tackled the problem of limited expressivity and differentiability in muscle-based face capture systems by making an existing expressive muscle model fully differentiable and drivable by blendshape bases. The result is a model that enables shape matching of 3D geometry and automatic 3D facial pose estimation from a single 2D RGB image without markers or depth.
Muscle-based systems have the potential to provide both anatomical accuracy and semantic interpretability as compared to blendshape models; however, a lack of expressivity and differentiability has limited their impact. Thus, we propose modifying a recently developed rather expressive muscle-based system in order to make it fully-differentiable; in fact, our proposed modifications allow this physically robust and anatomically accurate muscle model to conveniently be driven by an underlying blendshape basis. Our formulation is intuitive, natural, as well as monolithically and fully coupled such that one can differentiate the model from end to end, which makes it viable for both optimization and learning-based approaches for a variety of applications. We illustrate this with a number of examples including both shape matching of three-dimensional geometry as as well as the automatic determination of a three-dimensional facial pose from a single two-dimensional RGB image without using markers or depth information.