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arXiv · 2201.01016

Detailed Facial Geometry Recovery from Multi-View Images by Learning an Implicit Function

Yunze Xiao·Hao Zhu·Haotian Yang·Zhengyu Diao·Xiangju Lu·Xun Cao
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Abstract

Recovering detailed facial geometry from a set of calibrated multi-view images is valuable for its wide range of applications. Traditional multi-view stereo (MVS) methods adopt an optimization-based scheme to regularize the matching cost. Recently, learning-based methods integrate all these into an end-to-end neural network and show superiority of efficiency. In this paper, we propose a novel architecture to recover extremely detailed 3D faces within dozens of seconds. Unlike previous learning-based methods that regularize the cost volume via 3D CNN, we propose to learn an implicit function for regressing the matching cost. By fitting a 3D morphable model from multi-view images, the features of multiple images are extracted and aggregated in the mesh-attached UV space, which makes the implicit function more effective in recovering detailed facial shape. Our method outperforms SOTA learning-based MVS in accuracy by a large margin on the FaceScape dataset. The code and data are released in https://github.com/zhuhao-nju/mvfr.

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Yunze Xiao, Hao Zhu, Haotian Yang, Zhengyu Diao, Xiangju Lu, Xun Cao. 2022-05-05. Detailed Facial Geometry Recovery from Multi-View Images by Learning an Implicit Function. https://arxiv.org/abs/2201.01016

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