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arXiv · 2108.02931

Detailed Avatar Recovery from Single Image

Hao Zhu·Xinxin Zuo·Haotian Yang·Sen Wang·Xun Cao·Ruigang Yang
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Abstract

This paper presents a novel framework to recover \emph{detailed} avatar from a single image. It is a challenging task due to factors such as variations in human shapes, body poses, texture, and viewpoints. Prior methods typically attempt to recover the human body shape using a parametric-based template that lacks the surface details. As such resulting body shape appears to be without clothing. In this paper, we propose a novel learning-based framework that combines the robustness of the parametric model with the flexibility of free-form 3D deformation. We use the deep neural networks to refine the 3D shape in a Hierarchical Mesh Deformation (HMD) framework, utilizing the constraints from body joints, silhouettes, and per-pixel shading information. Our method can restore detailed human body shapes with complete textures beyond skinned models. Experiments demonstrate that our method has outperformed previous state-of-the-art approaches, achieving better accuracy in terms of both 2D IoU number and 3D metric distance.

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Hao Zhu, Xinxin Zuo, Haotian Yang, Sen Wang, Xun Cao, Ruigang Yang. 2021-08-06. Detailed Avatar Recovery from Single Image. https://arxiv.org/abs/2108.02931

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