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This paper presents a novel latent 3D diffusion model for the generation of neural voxel fields, aiming to achieve accurate part-aware structures.
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T. DeVries, M. A. Bautista, N. Srivastava, G. W. Taylor, and J. M. Susskind, “Unconstrained scene generation with locally conditioned radiance fields,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021
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2022
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K. Tertikas, P. Despoina, B. Pan, J. J. Park, M. A. Uy, I. Emiris, Y. Avrithis, and L. Guibas, “Partnerf: Generating part-aware editable 3d shapes without 3d supervision,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2023
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N. Müller, Y. Siddiqui, L. Porzi, S. R. Bulo, P. Kontschieder, and M. Nießner, “Diffrf: Rendering-guided 3d radiance field diffusion,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023
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2023
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B. Zhang, J. Tang, M. Niessner, and P. Wonka, “3dshape2vecset: A 3d shape representation for neural fields and generative diffusion models,” ACM Transactions on Graphics , 2023
2023
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G. K. Nakayama, M. A. Uy, J. Huang, S.-M. Hu, K. Li, and L. Guibas, “Difffacto: Controllable part-based 3d point cloud generation with cross diffusion,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023
2023
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2023
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2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
Z. Zhou and S. Tulsiani, “Sparsefusion: Distilling view-conditioned diffusion for 3d reconstruction,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023
2023
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H. Chen, J. Gu, A. Chen, W. Tian, Z. Tu, L. Liu, and H. Su, “Single-stage diffusion nerf: A unified approach to 3d generation and reconstruction,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023
2023
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2023
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J. Koo, S. Yoo, M. H. Nguyen, and M. Sung, “Salad: Part-level latent diffusion for 3d shape generation and manipulation,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
J. Hu, K.-H. Hui, Z. Liu, R. Li, and C.-W. Fu, “Neural wavelet-domain diffusion for 3d shape generation, inversion, and manipulation,” ACM Transactions on Graphics , 2024
2024
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Y. Cao, X. Meng, P. Mok, T.-Y. Lee, X. Liu, and P. Li, “Animediffusion: Anime diffusion colorization,” IEEE Transactions on Visualization and Computer Graphics , 2024
2024
Closest in time.