HumanNeRF: Efficiently Generated Human Radiance Field from Sparse Inputs

doi:10.1109/CVPR52688.2022.00759

	HumanNeRF: Efficiently Generated Human Radiance Field from Sparse Inputs
	Zhao, Fuqiang1 ; Yang, Wei 2; Zhang, Jiakai1 ; Lin, Pei1 ; Zhang, Yingliang 3; Yu, Jingyi1 ; Xu, Lan1,4
	2022
会议录名称	PROCEEDINGS OF THE IEEE COMPUTER SOCIETY CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION
ISSN	1063-6919
卷号	2022-June
页码	7733-7743
发表状态	已发表
DOI	10.1109/CVPR52688.2022.00759
摘要	Recent neural human representations can produce high-quality multi-view rendering but require using dense multi-view inputs and costly training. They are hence largely limited to static models as training each frame is infeasible. We present HumanNeRF - a neural representation with efficient generalization ability - for high-fidelity free-view synthesis of dynamic humans. Analogous to how IBRNet assists NeRF by avoiding perscene training, HumanNeRF employs an aggregated pixel-alignment feature across multi-view inputs along with a pose embedded non-rigid deformation field for tackling dynamic motions. The raw Human-NeRF can already produce reasonable rendering on sparse video inputs of unseen subjects and camera settings. To further improve the rendering quality, we augment our solution with in-hour scene-specific fine-tuning, and an appearance blending module for combining the benefits of both neural volumetric rendering and neural texture blending. Extensive experiments on various multi-view dynamic hu-man datasets demonstrate effectiveness of our approach in synthesizing photo-realistic free-view humans under challenging motions and with very sparse camera view inputs. © 2022 IEEE.
会议名称	2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022
出版地	10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA
会议地点	New Orleans, LA, United states
会议日期	June 19, 2022 - June 24, 2022
URL	查看原文
收录类别	EI ; CPCI-S
语种	英语
资助项目	Shanghai YangFan Program[21YF1429500] ; Shanghai Local college capacity building program[22010502800] ; NSFC programs["61976138","61977047"] ; National Key Research and Development Program[2018YFB2100500] ; STCSM[2015F0203-00006] ; SHMEC[2019-01-07-00-01-E00003]
WOS研究方向	Computer Science ; Imaging Science & Photographic Technology
WOS类目	Computer Science, Artificial Intelligence ; Imaging Science & Photographic Technology
WOS记录号	WOS:000870759100057
出版者	IEEE Computer Society
EI入藏号	20224913201514
原始文献类型	Conference article (CA)
来源库	IEEE
引用统计	正在获取...
文献类型	会议论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/256370
专题	信息科学与技术学院_博士生信息科学与技术学院_PI研究组_虞晶怡组信息科学与技术学院_硕士生信息科学与技术学院_PI研究组_许岚组
通讯作者	Zhao, Fuqiang
作者单位	1.ShanghaiTech Univ, Shanghai, Peoples R China 2.Huazhong Univ Sci & Technol, Wuhan, Peoples R China 3.DGene, Baton Rouge, LA USA 4.Shanghai Engn Res Ctr Intelligent Vis & Imaging, Shanghai, Peoples R China
第一作者单位	上海科技大学
通讯作者单位	上海科技大学
第一作者的第一单位	上海科技大学
推荐引用方式 GB/T 7714	Zhao, Fuqiang,Yang, Wei,Zhang, Jiakai,et al. HumanNeRF: Efficiently Generated Human Radiance Field from Sparse Inputs[C]. 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA:IEEE Computer Society,2022:7733-7743.