Disentangled Representation Learning for 3D Face Shape
This work addresses the need for improved 3D face modeling and animation in computer graphics and vision, though it appears incremental as it builds on prior disentanglement methods.
The paper tackled the problem of disentangling 3D face shapes into identity and expression parts using a nonlinear attribute decomposition framework with vertex-based deformation representation, resulting in better performance and more natural expression transfer compared to existing methods.
In this paper, we present a novel strategy to design disentangled 3D face shape representation. Specifically, a given 3D face shape is decomposed into identity part and expression part, which are both encoded and decoded in a nonlinear way. To solve this problem, we propose an attribute decomposition framework for 3D face mesh. To better represent face shapes which are usually nonlinear deformed between each other, the face shapes are represented by a vertex based deformation representation rather than Euclidean coordinates. The experimental results demonstrate that our method has better performance than existing methods on decomposing the identity and expression parts. Moreover, more natural expression transfer results can be achieved with our method than existing methods.