Surface-Aligned Neural Radiance Fields for Controllable 3D Human Synthesis

Tianhan Xu; Yasuhiro Fujita; Eiichi Matsumoto

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arXiv · 2201.01683

Surface-Aligned Neural Radiance Fields for Controllable 3D Human Synthesis

Tianhan Xu·Yasuhiro Fujita·Eiichi Matsumoto

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Abstract

We propose a new method for reconstructing controllable implicit 3D human models from sparse multi-view RGB videos. Our method defines the neural scene representation on the mesh surface points and signed distances from the surface of a human body mesh. We identify an indistinguishability issue that arises when a point in 3D space is mapped to its nearest surface point on a mesh for learning surface-aligned neural scene representation. To address this issue, we propose projecting a point onto a mesh surface using a barycentric interpolation with modified vertex normals. Experiments with the ZJU-MoCap and Human3.6M datasets show that our approach achieves a higher quality in a novel-view and novel-pose synthesis than existing methods. We also demonstrate that our method easily supports the control of body shape and clothes. Project page: https://pfnet-research.github.io/surface-aligned-nerf/.

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BibTeX RIS

Tianhan Xu, Yasuhiro Fujita, Eiichi Matsumoto. 2022-01-05. Surface-Aligned Neural Radiance Fields for Controllable 3D Human Synthesis. https://arxiv.org/abs/2201.01683

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