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Recently, many works have been proposed to utilize the neural radiance field for novel view synthesis of human performers.
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M. Loper, N. Mahmood, J. Romero, G. Pons-Moll, and M. J. Black, “SMPL: A skinned multi-person linear model,” ACM Trans. Graphics (Proc. SIGGRAPH Asia) , vol. 34, no. 6, pp. 248:1–248:16, Oct. 2015
2015
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2015
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2016
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F. Bogo, A. Kanazawa, C. Lassner, P. Gehler, J. Romero, and M. J. Black, “Keep it SMPL: Automatic estimation of 3D human pose and shape from a single image,” in Computer Vision – ECCV 2016 , ser. Lecture Notes in Computer Science. Springer International Publishing, Oct. 2016
2016
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J. Romero, D. Tzionas, and M. J. Black, “Embodied hands: Modeling and capturing hands and bodies together,” ACM Transactions on Graphics, (Proc. SIGGRAPH Asia) , vol. 36, no. 6, Nov. 2017
2017
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T. Li, T. Bolkart, M. J. Black, H. Li, and J. Romero, “Learning a model of facial shape and expression from 4D scans,” ACM Transactions on Graphics, (Proc. SIGGRAPH Asia) , vol. 36, no. 6, pp. 194:1–194:17, 2017. [Online]. Available: https://doi.org/10.1145/3130800.3130813
2017
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S. Zuffi, A. Kanazawa, D. W. Jacobs, and M. J. Black, “3d menagerie: Modeling the 3d shape and pose of animals,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 6365–6373
2017
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P. Hedman, J. Philip, T. Price, J.-M. Frahm, G. Drettakis, and G. Brostow, “Deep blending for free-viewpoint image-based rendering,” ACM Transactions on Graphics (TOG) , vol. 37, no. 6, pp. 1–15, 2018
2018
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T. Alldieck, M. Magnor, W. Xu, C. Theobalt, and G. Pons-Moll, “Video based reconstruction of 3d people models,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 8387–8397
2018
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N. Kolotouros, G. Pavlakos, and K. Daniilidis, “Convolutional mesh regression for single-image human shape reconstruction,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 4501–4510
2019
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G. Pavlakos, V. Choutas, N. Ghorbani, T. Bolkart, A. A. A. Osman, D. Tzionas, and M. J. Black, “Expressive body capture: 3D hands, face, and body from a single image,” in Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) , 2019, pp. 10 975–10 985
2019
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S. Saito, Z. Huang, R. Natsume, S. Morishima, A. Kanazawa, and H. Li, “Pifu: Pixel-aligned implicit function for high-resolution clothed human digitization,” in IEEE/CVF International Conference on Computer Vision (ICCV) , 2019, pp. 2304–2314
2019
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S. Lombardi, T. Simon, J. Saragih, G. Schwartz, A. Lehrmann, and Y. Sheikh, “Neural volumes: Learning dynamic renderable volumes from images,” ACM Trans. Graph. , vol. 38, no. 4, pp. 65:1–65:14, Jul. 2019
2019
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B. Mildenhall, P. P. Srinivasan, M. Tancik, J. T. Barron, R. Ramamoorthi, and R. Ng, “Nerf: Representing scenes as neural radiance fields for view synthesis,” in European conference on computer vision . Springer, 2020, pp. 405–421
2020
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2020
Earlier work this paper cites.
A. A. A. Osman, T. Bolkart, and M. J. Black, “STAR: A sparse trained articulated human body regressor,” in European Conference on Computer Vision (ECCV) , 2020, pp. 598–613. [Online]. Available: https://star.is.tue.mpg.de
2020
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S. Saito, T. Simon, J. Saragih, and H. Joo, “Pifuhd: Multi-level pixel-aligned implicit function for high-resolution 3d human digitization,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2020, pp. 84–93
2020
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B. Jiang, J. Zhang, Y. Hong, J. Luo, L. Liu, and H. Bao, “Bcnet: Learning body and cloth shape from a single image,” in European Conference on Computer Vision . Springer, 2020
2020
Earlier work this paper cites.
M. Tancik, P. P. Srinivasan, B. Mildenhall, S. Fridovich-Keil, N. Raghavan, U. Singhal, R. Ramamoorthi, J. T. Barron, and R. Ng, “Fourier features let networks learn high frequency functions in low dimensional domains,” arXiv: Computer Vision and Pattern Recognition , 2020
2020
Earlier work this paper cites.
S. Peng, Y. Zhang, Y. Xu, Q. Wang, Q. Shuai, H. Bao, and X. Zhou, “Neural body: Implicit neural representations with structured latent codes for novel view synthesis of dynamic humans,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2021, pp. 9054–9063
2021
Earlier work this paper cites.
S. Peng, J. Dong, Q. Wang, S. Zhang, Q. Shuai, X. Zhou, and H. Bao, “Animatable neural radiance fields for modeling dynamic human bodies,” in ICCV , 2021
2021
Cited alongside, same era.
S.-Y. Su, F. Yu, M. Zollhöfer, and H. Rhodin, “A-nerf: Articulated neural radiance fields for learning human shape, appearance, and pose,” Advances in Neural Information Processing Systems , vol. 34, 2021
2021
Cited alongside, same era.
Y. Kwon, D. Kim, D. Ceylan, and H. Fuchs, “Neural human performer: Learning generalizable radiance fields for human performance rendering,” Advances in Neural Information Processing Systems , vol. 34, 2021
2021
Cited alongside, same era.
L. Liu, M. Habermann, V. Rudnev, K. Sarkar, J. Gu, and C. Theobalt, “Neural actor: Neural free-view synthesis of human actors with pose control,” ACM Transactions on Graphics (TOG) , vol. 40, no. 6, pp. 1–16, 2021
2021
Cited alongside, same era.
R. Zhang and J. Chen, “Ndf: Neural deformable fields for dynamic human modelling,” 2022
2022
Later among the works it cites.
M. Niemeyer, J. T. Barron, B. Mildenhall, M. S. M. Sajjadi, A. Geiger, and N. Radwan, “Regnerf: Regularizing neural radiance fields for view synthesis from sparse inputs,” in Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) , 2022
2022
Later among the works it cites.
M. Tancik, V. Casser, X. Yan, S. Pradhan, B. Mildenhall, P. P. Srinivasan, J. T. Barron, and H. Kretzschmar, “Block-nerf: Scalable large scene neural view synthesis,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 8248–8258
2022
Later among the works it cites.
T. Xu, Y. Fujita, and E. Matsumoto, “Surface-aligned neural radiance fields for controllable 3d human synthesis,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2022, pp. 15 883–15 892
2022
Later among the works it cites.
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H. Xu, T. Alldieck, and C. Sminchisescu, “H-nerf: Neural radiance fields for rendering and temporal reconstruction of humans in motion,” Advances in Neural Information Processing Systems , vol. 34, 2021
2021
Cited alongside, same era.
Y. Kwon, D. Kim, D. Ceylan, and H. Fuchs, “Neural human performer: Learning generalizable radiance fields for human performance rendering,” arXiv: Computer Vision and Pattern Recognition , 2021
2021
Cited alongside, same era.
“Easymocap - make human motion capture easier.” Github, 2021. [Online]. Available: https://github.com/zju3dv/EasyMocap
2021
Cited alongside, same era.
A. Pumarola, E. Corona, G. Pons-Moll, and F. Moreno-Noguer, “D-nerf: Neural radiance fields for dynamic scenes,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 10 318–10 327
2021
Cited alongside, same era.
S. J. Garbin, M. Kowalski, M. Johnson, J. Shotton, and J. Valentin, “Fastnerf: High-fidelity neural rendering at 200fps,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 14 346–14 355
2021
Cited alongside, same era.
2021
Cited alongside, same era.
2021
Cited alongside, same era.
J. T. Barron, B. Mildenhall, M. Tancik, P. Hedman, R. Martin-Brualla, and P. P. Srinivasan, “Mip-nerf: A multiscale representation for anti-aliasing neural radiance fields,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 5855–5864
2021
Cited alongside, same era.
Y. Zhi, S. Qian, X. Yan, and S. Gao, “Dual-space nerf: Learning animatable avatars and scene lighting in separate spaces,” 2022
2022
Later among the works it cites.
T. Alldieck, M. Zanfir, and C. Sminchisescu, “Photorealistic monocular 3d reconstruction of humans wearing clothing,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2022
2022
Later among the works it cites.
Y. Xiu, J. Yang, D. Tzionas, and M. J. Black, “ICON: Implicit Clothed humans Obtained from Normals,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2022, pp. 13 296–13 306
2022
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2022
Later among the works it cites.
B. Jiang, Y. Hong, H. Bao, and J. Zhang, “Selfrecon: Self reconstruction your digital avatar from monocular video,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2022
2022
Later among the works it cites.
C. Sun, M. Sun, and H. Chen, “Direct voxel grid optimization: Super-fast convergence for radiance fields reconstruction,” in CVPR , 2022
2022
Later among the works it cites.
H. Wang, J. Ren, Z. Huang, K. Olszewski, M. Chai, Y. Fu, and S. Tulyakov, “R2l: Distilling neural radiance field to neural light field for efficient novel view synthesis,” in Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XXXI . Springer, 2022, pp. 612–629
2022
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K. Deng, A. Liu, J.-Y. Zhu, and D. Ramanan, “Depth-supervised NeRF: Fewer views and faster training for free,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2022
2022
Later among the works it cites.
Sara Fridovich-Keil and Alex Yu, M. Tancik, Q. Chen, B. Recht, and A. Kanazawa, “Plenoxels: Radiance fields without neural networks,” in CVPR , 2022
2022
Later among the works it cites.
L. Wu, J. Y. Lee, A. Bhattad, Y.-X. Wang, and D. Forsyth, “Diver: Real-time and accurate neural radiance fields with deterministic integration for volume rendering,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 16 200–16 209
2022
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X. Gao, C. Zhong, J. Xiang, Y. Hong, Y. Guo, and J. Zhang, “Reconstructing personalized semantic facial nerf models from monocular video,” ACM Transactions on Graphics (Proceedings of SIGGRAPH Asia) , vol. 41, no. 6, 2022
2022
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J. Tang, “Torch-ngp: a pytorch implementation of instant-ngp,” 2022, https://github.com/ashawkey/torch-ngp
2022
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2022
Later among the works it cites.
Z. Zheng, H. Huang, T. Yu, H. Zhang, Y. Guo, and Y. Liu, “Structured local radiance fields for human avatar modeling,” 2023
2023
Closest in time.
S. Wang, K. Schwarz, A. Geiger, and S. Tang, “Arah: Animatable volume rendering of articulated human sdfs,” 2023
2023
Closest in time.
A. Chen, Z. Xu, A. Geiger, J. Yu, and H. Su, “Tensorf: Tensorial radiance fields,” 2023
2023
Closest in time.