Disentangled representation learning for 3d face shape
Proceedings of the IEEE/CVF Conference on Computer Vision and …, 2019•openaccess.thecvf.com
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 …
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 …
Abstract
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.
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