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In this paper, we present a novel double diffusion based neural radiance field, dubbed DD-NeRF, to reconstruct human body geometry and render the human body appearance in novel views from a sparse set of images.
Marching cubes: A high resolution 3d surface construction algorithm
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Diederik P. Kingma and Jimmy Ba · 2014
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Smpl: A skinned multi-person linear model
Matthew Loper, Naureen Mahmood, and Javier Romero · 2015
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Stacked hourglass networks for human pose estimation
Alejandro Newell, Kaiyu Yang, and Jia Deng · 2016
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Attention is all you need
Ashish Vaswani and Noam Shazeer · 2017
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Volumetric performance capture from minimal camera viewpoints
Andrew Gilbert, Marco Volino, John Collomosse, and Adrian Hilton · 2018
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3d semantic segmentation with submanifold sparse convolutional networks
Benjamin Graham and Engelcke · 2018
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Doublefusion: Real-time capture of human performances with inner body shapes from a single depth sensor
Tao Yu, Zerong Zheng, and Yebin Liu · 2018
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The unreasonable effectiveness of deep features as a perceptual metric
Richard Zhang, Phillip Isola, and Alexei Efros · 2018
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trimesh, 2019
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Amos Gropp and Lior Yariv · 2020
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Ben Mildenhall, Pratul P Srinivasan, and Ren Ng · 2020
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Michael Niemeyer, Lars Mescheder, and Andreas Geiger · 2020
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Pifuhd: Multi-level pixel-aligned implicit function for high-resolution 3d human digitization
Neural lumigraph rendering
Petr Kellnhofer, Lars C Jebe, and Gordon Wetzstein · 2021
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Neural human performer: Learning generalizable radiance fields for human performance rendering, 2021
Youngjoong Kwon, Dahun Kim, Duygu Ceylan, and Henry Fuchs · 2021
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Neural body: Implicit neural representations with structured latent codes for novel view synthesis of dynamic humans
Sida Peng, Yuanqing Zhang, Hujun Bao, and Xiaowei Zhou · 2021
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Doublefield: Bridging the neural surface and radiance fields for high-fidelity human rendering
Ruizhi Shao, Hongwen Zhang, Tao Yu, and Yebin Liu · 2021
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Peng Wang, Lingjie Liu, Yuan Liu, and Wenping Wang · 2021
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