HumanNeRF: Efficiently Generated Human Radiance Field from Sparse Inputs
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 general...
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| Vydané v: | Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) s. 7733 - 7743 |
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01.06.2022
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Zhang, Jiakai Yu, Jingyi Zhang, Yingliang Lin, Pei Zhao, Fuqiang Yang, Wei Xu, Lan |
| Author_xml | – sequence: 1 givenname: Fuqiang surname: Zhao fullname: Zhao, Fuqiang organization: ShanghaiTech University – sequence: 2 givenname: Wei surname: Yang fullname: Yang, Wei organization: Huazhong University of Science and Technology – sequence: 3 givenname: Jiakai surname: Zhang fullname: Zhang, Jiakai organization: ShanghaiTech University – sequence: 4 givenname: Pei surname: Lin fullname: Lin, Pei organization: ShanghaiTech University – sequence: 5 givenname: Yingliang surname: Zhang fullname: Zhang, Yingliang organization: DGene – sequence: 6 givenname: Jingyi surname: Yu fullname: Yu, Jingyi organization: ShanghaiTech University – sequence: 7 givenname: Lan surname: Xu fullname: Xu, Lan organization: ShanghaiTech University |
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| Snippet | Recent neural human representations can produce high-quality multi-view rendering but require using dense multi-view inputs and costly training. They are hence... |
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| SubjectTerms | Cameras Computer vision Dynamics Entertainment industry Image and video synthesis and generation; 3D from multi-view and sensors; Face and gestures; Motion and tracking; Pose estimation and tracking Rendering (computer graphics) Telepresence Training |
| Title | HumanNeRF: Efficiently Generated Human Radiance Field from Sparse Inputs |
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