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arXiv · 2303.14092

NeuFace: Realistic 3D Neural Face Rendering from Multi-view Images

Mingwu Zheng·Haiyu Zhang·Hongyu Yang·Di Huang
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Abstract

Realistic face rendering from multi-view images is beneficial to various computer vision and graphics applications. Due to the complex spatially-varying reflectance properties and geometry characteristics of faces, however, it remains challenging to recover 3D facial representations both faithfully and efficiently in the current studies. This paper presents a novel 3D face rendering model, namely NeuFace, to learn accurate and physically-meaningful underlying 3D representations by neural rendering techniques. It naturally incorporates the neural BRDFs into physically based rendering, capturing sophisticated facial geometry and appearance clues in a collaborative manner. Specifically, we introduce an approximated BRDF integration and a simple yet new low-rank prior, which effectively lower the ambiguities and boost the performance of the facial BRDFs. Extensive experiments demonstrate the superiority of NeuFace in human face rendering, along with a decent generalization ability to common objects.

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Mingwu Zheng, Haiyu Zhang, Hongyu Yang, Di Huang. 2023-03-24. NeuFace: Realistic 3D Neural Face Rendering from Multi-view Images. https://arxiv.org/abs/2303.14092

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