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Advances in 3D generation have facilitated sequential 3D model generation (a.k.a 4D generation), yet its application for animatable objects with large motion remains scarce.
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Tatarchenko, M., Richter, S.R., Ranftl, R., Li, Z., Koltun, V., Brox, T.: What do single-view 3d reconstruction networks learn? In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019)
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Barron, J.T., Mildenhall, B., Tancik, M., Hedman, P., Martin-Brualla, R., Srinivasan, P.P.: Mip-nerf: A multiscale representation for anti-aliasing neural radiance fields. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (2021)
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Chen, X., Zheng, Y., Black, M.J., Hilliges, O., Geiger, A.: Snarf: Differentiable forward skinning for animating non-rigid neural implicit shapes. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (2021)
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Du, Y., Zhang, Y., Yu, H.X., Tenenbaum, J.B., Wu, J.: Neural radiance flow for 4d view synthesis and video processing. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). pp. 14304–14314. IEEE Computer Society (2021)
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Li, Z., Niklaus, S., Snavely, N., Wang, O.: Neural scene flow fields for space-time view synthesis of dynamic scenes. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2021)
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Mildenhall, B., Srinivasan, P.P., Tancik, M., Barron, J.T., Ramamoorthi, R., Ng, R.: Nerf: Representing scenes as neural radiance fields for view synthesis. Communications of the ACM 65
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Park, D.H., Azadi, S., Liu, X., Darrell, T., Rohrbach, A.: Benchmark for compositional text-to-image synthesis. In: Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 1) (2021)
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Park, K., Sinha, U., Barron, J.T., Bouaziz, S., Goldman, D.B., Seitz, S.M., Martin-Brualla, R.: Nerfies: Deformable neural radiance fields. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). pp. 5865–5874 (2021)
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Peng, S., Zhang, Y., Xu, Y., Wang, Q., Shuai, Q., Bao, H., Zhou, X.: 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)
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Pumarola, A., Corona, E., Pons-Moll, G., Moreno-Noguer, F.: D-NeRF: Neural Radiance Fields for Dynamic Scenes. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 10313–10322. IEEE, Nashville, TN, USA (Jun 2021). https://doi.org/10.1109/CVPR46437.2021.01018, https://ieeexplore.ieee.org/document/9578753/
2021
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Shen, T., Gao, J., Yin, K., Liu, M.Y., Fidler, S.: Deep marching tetrahedra: a hybrid representation for high-resolution 3d shape synthesis. In: Advances in Neural Information Processing Systems (NeurIPS) (2021)
2021
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Su, S.Y., Yu, F., Zollhöfer, M., Rhodin, H.: A-nerf: Articulated neural radiance fields for learning human shape, appearance, and pose. In: Advances in Neural Information Processing Systems (NeurIPS) (2021)
2021
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Wang, P., Liu, L., Liu, Y., Theobalt, C., Komura, T., Wang, W.: Neus: Learning neural implicit surfaces by volume rendering for multi-view reconstruction. Advances in Neural Information Processing Systems (NeurIPS) (2021)
2021
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Xian, W., Huang, J.B., Kopf, J., Kim, C.: Space-time neural irradiance fields for free-viewpoint video. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 9421–9431 (2021)
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Yang, G., Ramanan, D.: Learning to segment rigid motions from two frames. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2021)
2021
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Yang, G., Sun, D., Jampani, V., Vlasic, D., Cole, F., Liu, C., Ramanan, D.: Viser: Video-specific surface embeddings for articulated 3d shape reconstruction. Advances in Neural Information Processing Systems (NeurIPS) (2021)
2021
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Barron, J.T., Mildenhall, B., Verbin, D., Srinivasan, P.P., Hedman, P.: Mip-nerf 360: Unbounded anti-aliased neural radiance fields. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022)
2022
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Niemeyer, M., Barron, J.T., Mildenhall, B., Sajjadi, M.S., Geiger, A., Radwan, N.: Regnerf: Regularizing neural radiance fields for view synthesis from sparse inputs. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022)
2022
Cited alongside, same era.
Noguchi, A., Iqbal, U., Tremblay, J., Harada, T., Gallo, O.: Watch it move: Unsupervised discovery of 3d joints for re-posing of articulated objects. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 3677–3687 (2022)
2022
Cited alongside, same era.
2022
Cited alongside, same era.
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022)
2022
Lin, C.H., Gao, J., Tang, L., Takikawa, T., Zeng, X., Huang, X., Kreis, K., Fidler, S., Liu, M.Y., Lin, T.Y.: Magic3d: High-resolution text-to-3d content creation. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2023)
2023
Closest in time.
Oquab, M., Darcet, T., Moutakanni, T., Vo, H., Szafraniec, M., Khalidov, V., Fernandez, P., Haziza, D., Massa, F., El-Nouby, A., Assran, M., Ballas, N., Galuba, W., Howes, R., Huang, P.Y., Li, S.W., Misra, I., Rabbat, M., Sharma, V., Synnaeve, G., Xu, H., Jegou, H., Mairal, J., Labatut, P., Joulin, A., Bojanowski, P.: Dinov2: Learning robust visual features without supervision (2023)
2023
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Shao, R., Zheng, Z., Tu, H., Liu, B., Zhang, H., Liu, Y.: Tensor4D: Efficient Neural 4D Decomposition for High-Fidelity Dynamic Reconstruction and Rendering. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 16632–16642. IEEE, Vancouver, BC, Canada (Jun 2023). https://doi.org/10.1109/CVPR52729.2023.01596, https://ieeexplore.ieee.org/document/10204587/
2023
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Cited alongside, same era.
Saharia, C., Chan, W., Saxena, S., Li, L., Whang, J., Denton, E.L., Ghasemipour, K., Gontijo Lopes, R., Karagol Ayan, B., Salimans, T., et al.: Photorealistic text-to-image diffusion models with deep language understanding. Advances in Neural Information Processing Systems (NeurIPS) (2022)
2022
Cited alongside, same era.
Schuhmann, C., Beaumont, R., Vencu, R., Gordon, C., Wightman, R., Cherti, M., Coombes, T., Katta, A., Mullis, C., Wortsman, M., et al.: Laion-5b: An open large-scale dataset for training next generation image-text models. Advances in Neural Information Processing Systems (NeurIPS) 35
2022
Cited alongside, same era.
2022
Cited alongside, same era.
Wang, L., Zhang, J., Liu, X., Zhao, F., Zhang, Y., Zhang, Y., Wu, M., Yu, J., Xu, L.: Fourier plenoctrees for dynamic radiance field rendering in real-time. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022)
2022
Cited alongside, same era.
Yang, G., Vo, M., Neverova, N., Ramanan, D., Vedaldi, A., Joo, H.: Banmo: Building animatable 3d neural models from many casual videos. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022)
2022
Cited alongside, same era.
Zhang, K., Kolkin, N., Bi, S., Luan, F., Xu, Z., Shechtman, E., Snavely, N.: Arf: Artistic radiance fields. In: European Conference on Computer Vision. pp. 717–733. Springer (2022)
2022
Cited alongside, same era.
Cao, A., Johnson, J.: Hexplane: A fast representation for dynamic scenes. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2023)
2023
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2023
Cited alongside, same era.
2023
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2023
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Stathopoulos, A., Pavlakos, G., Han, L., Metaxas, D.N.: Learning articulated shape with keypoint pseudo-labels from web images. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 13092–13101 (2023)
2023
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2023
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Wang, Y., Dong, Y., Sun, F., Yang, X.: Root pose decomposition towards generic non-rigid 3d reconstruction with monocular videos. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (2023)
2023
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Wang, Y., Han, Q., Habermann, M., Daniilidis, K., Theobalt, C., Liu, L.: Neus2: Fast learning of neural implicit surfaces for multi-view reconstruction. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (2023)
2023
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2023
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2023
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Wu, S., Li, R., Jakab, T., Rupprecht, C., Vedaldi, A.: Magicpony: Learning articulated 3d animals in the wild. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2023)
2023
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Yang, G., Wang, C., Reddy, N.D., Ramanan, D.: Reconstructing animatable categories from videos. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2023)
2023
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Yang, G., Yang, S., Zhang, J.Z., Manchester, Z., Ramanan, D.: Ppr: Physically plausible reconstruction from monocular videos. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). pp. 3914–3924 (October 2023)
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Yang, J., Gao, M., Li, Z., Gao, S., Wang, F., Zheng, F.: Track anything: Segment anything meets videos (2023)
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Zhang, L., Rao, A., Agrawala, M.: Adding conditional control to text-to-image diffusion models. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (2023)
2023
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