Disentangling Features in 3D Face Shapes for Joint Face Reconstruction and Recognition

CVPR 2018 Feng LiuRonghang ZhuDan ZengQijun ZhaoXiaoming Liu

This paper proposes an encoder-decoder network to disentangle shape features during 3D face reconstruction from single 2D images, such that the tasks of reconstructing accurate 3D face shapes and learning discriminative shape features for face recognition can be accomplished simultaneously. Unlike existing 3D face reconstruction methods, our proposed method directly regresses dense 3D face shapes from single 2D images, and tackles identity and residual (i.e., non-identity) components in 3D face shapes explicitly and separately based on a composite 3D face shape model with latent representations... (read more)

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