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Existing neural rendering methods for creating human avatars typically either require dense input signals such as video or multi-view images, or leverage a learned prior from large-scale specific 3D human datasets such that reconstruction can be performed with sparse-view inputs.
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Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Matthew Loper, Naureen Mahmood, Javier Romero, Gerard Pons-Moll, and Michael J. Black · 2015
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Perceptual losses for real-time style transfer and super-resolution
Justin Johnson, Alexandre Alahi, and Li Fei-Fei · 2016
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Ziwei Liu, Ping Luo, Shi Qiu, Xiaogang Wang, and Xiaoou Tang · 2016
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Ceyuan Yang, Zhe Wang, Xinge Zhu, Chen Huang, Jianping Shi, and Dahua Lin · 2018
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Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang · 2018
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Everybody Dance Now
Caroline Chan, Shiry Ginosar, Tinghui Zhou, and Alexei A. Efros · 2019
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3d human avatar digitization from a single image
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ARCH: Animatable Reconstruction of Clothed Humans
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GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis
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Multi-View Neural Human Rendering
Minye Wu, Yuehao Wang, Qiang Hu, and Jingyi Yu · 2020
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Perceiving 3D Human-Object Spatial Arrangements from a Single Image in the Wild
Jason Y. Zhang, Sam Pepose, Hanbyul Joo, Deva Ramanan, Jitendra Malik, and Angjoo Kanazawa · 2020
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Emerging properties in self-supervised vision transformers
Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin · 2021
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MVSNeRF: Fast Generalizable Radiance Field Reconstruction From Multi-View Stereo
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Smplicit: Topology-aware generative model for clothed people
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Kyungmin Jo, Gyumin Shim, Sanghun Jung, Soyoung Yang, and Jaegul Choo · 2021
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