中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 计算技术研究所
  3. 中国科学院计算技术研究所
  4. 中国科学院计算技术研究所期刊论文
  5. 英文
ReN Human: Learning Relightable Neural Implicit Surfaces for Animatable Human Rendering

文献类型：期刊论文

作者Xie, Rengan4; Huang, Kai1,2,4; Cho, In-Young3; Yang, Sen2; Chen, Wei4; Bao, Hujun4; Zheng, Wenting4; Li, Rong2; Huo, Yuchi2,4
刊名ACM TRANSACTIONS ON GRAPHICS
出版日期2024-10-01
卷号43期号:5页码:22
关键词Relighting sign distance function animatable human inverse rendering volume rendering
ISSN号0730-0301
DOI10.1145/3678002
英文摘要Recently, implicit neural representation has been widely used to learn the appearance of human bodies in the canonical space, which can be further animated using a parametric human model. However, how to decompose the material properties from the implicit representation for relighting has not yet been investigated thoroughly. We propose to address this problem with a novel framework, ReN Human, that takes sparse or even monocular input videos collected in unconstrained lighting to produce a 3D human representation that can be rendered with novel views, poses, and lighting. Our method represents humans as deformable implicit neural representation and decomposes the geometry, material of humans as well as environment illumination for capturing a relightable and animatable human model. Moreover, we introduce a volumetric lighting grid consisting of spherical Gaussian mixtures to learn the spatially varying illumination and animatable visibility probes to model the dynamic self-occlusion caused by human motion. Specifically, we learn the material property fields and illumination using a physically-based rendering layer that uses Monte Carlo importance sampling to facilitate differentiation of the complex rendering integral. We demonstrate that our approach outperforms recent novel views and poses synthesis methods in a challenging benchmark with sparse videos, enabling high-fidelity human relighting.
资助项目NSFC[62441205] ; Zhejiang Province Jianbing Research and Development Project[2023C01042] ; National Natural Science Foundation of China[U22B2034] ; National Key R&D Program of China[2024YDLN0011] ; Information Technology Center ; State Key Lab of CAD&CG, Zhejiang University
WOS研究方向Computer Science
语种英语
WOS记录号WOS:001326849100001
出版者ASSOC COMPUTING MACHINERY
源URL[http://119.78.100.204/handle/2XEOYT63/39549]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Xie, Rengan
作者单位1.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China
2.Zhejiang Lab, Hangzhou, Peoples R China
3.KRAFTON, Seoul, South Korea
4.Zhejiang Univ, State Key Lab CAD&CG, Hangzhou, Peoples R China
推荐引用方式
GB/T 7714		 Xie, Rengan,Huang, Kai,Cho, In-Young,et al. ReN Human: Learning Relightable Neural Implicit Surfaces for Animatable Human Rendering[J]. ACM TRANSACTIONS ON GRAPHICS,2024,43(5):22.
APA Xie, Rengan.,Huang, Kai.,Cho, In-Young.,Yang, Sen.,Chen, Wei.,...&Huo, Yuchi.(2024).ReN Human: Learning Relightable Neural Implicit Surfaces for Animatable Human Rendering.ACM TRANSACTIONS ON GRAPHICS,43(5),22.
MLA Xie, Rengan,et al."ReN Human: Learning Relightable Neural Implicit Surfaces for Animatable Human Rendering".ACM TRANSACTIONS ON GRAPHICS 43.5(2024):22.

入库方式： OAI收割

来源：计算技术研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。