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|a 10.1109/TPAMI.2021.3102128
|2 doi
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|a eng
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| 100 |
1 |
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|a Zhu, Hao
|e verfasserin
|4 aut
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| 245 |
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|a Detailed Avatar Recovery From Single Image
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|c 2022
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|a Text
|b txt
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Completed 06.10.2022
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|a Date Revised 19.11.2022
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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| 520 |
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|a This paper presents a novel framework to recover 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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| 650 |
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|a Journal Article
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| 650 |
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4 |
|a Research Support, Non-U.S. Gov't
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| 700 |
1 |
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|a Zuo, Xinxin
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Yang, Haotian
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wang, Sen
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Cao, Xun
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Yang, Ruigang
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 44(2022), 11 vom: 04. Nov., Seite 7363-7379
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|x 1939-3539
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|g volume:44
|g year:2022
|g number:11
|g day:04
|g month:11
|g pages:7363-7379
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|u http://dx.doi.org/10.1109/TPAMI.2021.3102128
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