Detailed Avatar Recovery from Single Image
Hao Zhu, Xinxin Zuo, Haotian Yang, Sen Wang, Xun Cao and, Ruigang Yang

TL;DR
This paper introduces a learning-based framework that reconstructs detailed 3D human avatars from a single image, capturing surface details and textures beyond traditional parametric models.
Contribution
It combines parametric models with free-form 3D deformation using neural networks within a Hierarchical Mesh Deformation framework for detailed avatar recovery.
Findings
Outperforms previous methods in accuracy
Achieves detailed surface and texture reconstruction
Effective in various poses and viewpoints
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…
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Taxonomy
Topics3D Shape Modeling and Analysis · Advanced Vision and Imaging · Human Pose and Action Recognition
