3D Facial Geometry Recovery from a Depth View with Attention Guided Generative Adversarial Network

• URL: http://arxiv.org/abs/2009.00938v1
• Date: Wed, 2 Sep 2020 10:35:26 GMT
• Title: 3D Facial Geometry Recovery from a Depth View with Attention Guided Generative Adversarial Network
• Authors: Xiaoxu Cai, Hui Yu, Jianwen Lou, Xuguang Zhang, Gongfa Li, Junyu Dong
• Abstract summary: We present to recover the complete 3D facial geometry from a single depth view by proposing an Attention Guided Generative Adversarial Networks (AGGAN) Specifically, AGGAN encodes the 3D facial geometry within a voxel space and utilizes an attention-guided GAN to model the illposed 2.5D depth-3D mapping. Both qualitative and quantitative comparisons show that AGGAN recovers a more complete and smoother 3D facial shape, with the capability to handle a much wider range of view angles and resist to noise in the depth view than conventional methods.
• Score: 27.773904952734547
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present to recover the complete 3D facial geometry from a single depth view by proposing an Attention Guided Generative Adversarial Networks (AGGAN). In contrast to existing work which normally requires two or more depth views to recover a full 3D facial geometry, the proposed AGGAN is able to generate a dense 3D voxel grid of the face from a single unconstrained depth view. Specifically, AGGAN encodes the 3D facial geometry within a voxel space and utilizes an attention-guided GAN to model the illposed 2.5D depth-3D mapping. Multiple loss functions, which enforce the 3D facial geometry consistency, together with a prior distribution of facial surface points in voxel space are incorporated to guide the training process. Both qualitative and quantitative comparisons show that AGGAN recovers a more complete and smoother 3D facial shape, with the capability to handle a much wider range of view angles and resist to noise in the depth view than conventional methods

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