DreamCraft3D++: Efficient Hierarchical 3D Generation with Multi-Plane Reconstruction Model

A. X. Chang, T. Funkhouser, L. Guibas, P. Hanrahan, Q. Huang, Z. Li, S. Savarese, M. Savva, S. Song, H. Su et al. , “Shapenet: An information-rich 3d model repository,” arXiv preprint arXiv:1512.03012 , 2015

Original

2015

Earlier work this paper cites.

A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark et al. , “Learning transferable visual models from natural language supervision,” in International conference on machine learning . PMLR, 2021, pp. 8748–8763

2021

Earlier work this paper cites.

E. R. Chan, M. Monteiro, P. Kellnhofer, J. Wu, and G. Wetzstein, “pi-gan: Periodic implicit generative adversarial networks for 3d-aware image synthesis,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2021, pp. 5799–5809

2021

Earlier work this paper cites.

C. Xie, C. Wang, B. Zhang, H. Yang, D. Chen, and F. Wen, “Style-based point generator with adversarial rendering for point cloud completion,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 4619–4628

2021

Earlier work this paper cites.

E. J. Hu, Y. Shen, P. Wallis, Z. Allen-Zhu, Y. Li, S. Wang, L. Wang, and W. Chen, “Lora: Low-rank adaptation of large language models,” arXiv preprint arXiv:2106.09685 , 2021

Original

2021

Earlier work this paper cites.

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,” Communications of the ACM , vol. 65, no. 1, pp. 99–106, 2021

2021

Earlier work this paper cites.

R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-resolution image synthesis with latent diffusion models,” 2021

2021

Earlier work this paper cites.

C. Saharia, W. Chan, S. Saxena, L. Li, J. Whang, E. L. Denton, K. Ghasemipour, R. Gontijo Lopes, B. Karagol Ayan, T. Salimans et al. , “Photorealistic text-to-image diffusion models with deep language understanding,” Advances in Neural Information Processing Systems , vol. 35, pp. 36 479–36 494, 2022

2022

Earlier work this paper cites.

A. Ramesh, P. Dhariwal, A. Nichol, C. Chu, and M. Chen, “Hierarchical text-conditional image generation with clip latents,” arXiv preprint arXiv:2204.06125 , vol. 1, no. 2, p. 3, 2022

Original

2022

Earlier work this paper cites.

R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-resolution image synthesis with latent diffusion models,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2022, pp. 10 684–10 695

2022

Earlier work this paper cites.

S. Gu, D. Chen, J. Bao, F. Wen, B. Zhang, D. Chen, L. Yuan, and B. Guo, “Vector quantized diffusion model for text-to-image synthesis,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 10 696–10 706

2022

Earlier work this paper cites.

E. R. Chan, C. Z. Lin, M. A. Chan, K. Nagano, B. Pan, S. De Mello, O. Gallo, L. J. Guibas, J. Tremblay, S. Khamis et al. , “Efficient geometry-aware 3d generative adversarial networks,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 16 123–16 133

2022

Earlier work this paper cites.

B. Poole, A. Jain, J. T. Barron, and B. Mildenhall, “Dreamfusion: Text-to-3d using 2d diffusion,” arXiv preprint arXiv:2209.14988 , 2022

Original

2022

Earlier work this paper cites.

T. Müller, A. Evans, C. Schied, and A. Keller, “Instant neural graphics primitives with a multiresolution hash encoding,” ACM Trans. Graph. , vol. 41, no. 4, pp. 102:1–102:15, Jul. 2022. [Online]. Available: https://doi.org/10.1145/3528223.3530127

2022

Earlier work this paper cites.

D. Watson, W. Chan, R. Martin-Brualla, J. Ho, A. Tagliasacchi, and M. Norouzi, “Novel view synthesis with diffusion models,” arXiv preprint arXiv:2210.04628 , 2022

Original

2022

Earlier work this paper cites.

A. Jain, B. Mildenhall, J. T. Barron, P. Abbeel, and B. Poole, “Zero-shot text-guided object generation with dream fields,” 2022

2022

Earlier work this paper cites.

H.-H. Lee and A. X. Chang, “Understanding pure clip guidance for voxel grid nerf models,” arXiv preprint arXiv:2209.15172 , 2022

Original

2022

Earlier work this paper cites.

F. Hong, M. Zhang, L. Pan, Z. Cai, L. Yang, and Z. Liu, “Avatarclip: Zero-shot text-driven generation and animation of 3d avatars,” ACM Transactions on Graphics (TOG) , vol. 41, no. 4, pp. 1–19, 2022

2022

Earlier work this paper cites.

H. Wang, X. Du, J. Li, R. A. Yeh, and G. Shakhnarovich, “Score jacobian chaining: Lifting pretrained 2d diffusion models for 3d generation,” arXiv preprint arXiv:2212.00774 , 2022

Original

2022

Earlier work this paper cites.

X. Zeng, A. Vahdat, F. Williams, Z. Gojcic, O. Litany, S. Fidler, and K. Kreis, “Lion: Latent point diffusion models for 3d shape generation,” arXiv preprint arXiv:2210.06978 , 2022

Original

2022

Earlier work this paper cites.

J. Gao, T. Shen, Z. Wang, W. Chen, K. Yin, D. Li, O. Litany, Z. Gojcic, and S. Fidler, “Get3d: A generative model of high quality 3d textured shapes learned from images,” Advances In Neural Information Processing Systems , vol. 35, pp. 31 841–31 854, 2022

2022

Earlier work this paper cites.

J. Tang, B. Zhang, B. Yang, T. Zhang, D. Chen, L. Ma, and F. Wen, “Explicitly controllable 3d-aware portrait generation,” arXiv preprint arXiv:2209.05434 , 2022

Original

2022

Earlier work this paper cites.

J. Sun, X. Wang, Y. Shi, L. Wang, J. Wang, and Y. Liu, “Ide-3d: Interactive disentangled editing for high-resolution 3d-aware portrait synthesis,” ACM Trans. Graph. , vol. 41, no. 6, nov 2022. [Online]. Available: https://doi.org/10.1145/3550454.3555506

2022

Earlier work this paper cites.

A. Sanghi, H. Chu, J. G. Lambourne, Y. Wang, C.-Y. Cheng, M. Fumero, and K. R. Malekshan, “Clip-forge: Towards zero-shot text-to-shape generation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 18 603–18 613

2022

Earlier work this paper cites.

P. Mittal, Y.-C. Cheng, M. Singh, and S. Tulsiani, “Autosdf: Shape priors for 3d completion, reconstruction and generation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 306–315

2022

Earlier work this paper cites.

X. Yan, L. Lin, N. J. Mitra, D. Lischinski, D. Cohen-Or, and H. Huang, “Shapeformer: Transformer-based shape completion via sparse representation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 6239–6249

2022

Earlier work this paper cites.

B. Zhang, M. Nießner, and P. Wonka, “3dilg: Irregular latent grids for 3d generative modeling,” in NeurIPS , 2022

2022

Earlier work this paper cites.

G. Nam, M. Khlifi, A. Rodriguez, A. Tono, L. Zhou, and P. Guerrero, “3d-ldm: Neural implicit 3d shape generation with latent diffusion models,” arXiv preprint arXiv:2212.00842 , 2022

Original

2022

Earlier work this paper cites.

A. Nichol, H. Jun, P. Dhariwal, P. Mishkin, and M. Chen, “Point-e: A system for generating 3d point clouds from complex prompts,” arXiv preprint arXiv:2212.08751 , 2022

Original

2022

Earlier work this paper cites.

M. A. Bautista, P. Guo, S. Abnar, W. Talbott, A. Toshev, Z. Chen, L. Dinh, S. Zhai, H. Goh, D. Ulbricht et al. , “Gaudi: A neural architect for immersive 3d scene generation,” Advances in Neural Information Processing Systems , vol. 35, pp. 25 102–25 116, 2022

2022

Earlier work this paper cites.

J. Munkberg, J. Hasselgren, T. Shen, J. Gao, W. Chen, A. Evans, T. Müller, and S. Fidler, “Extracting triangular 3d models, materials, and lighting from images,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 8280–8290

2022

Earlier work this paper cites.

L. Downs, A. Francis, N. Koenig, B. Kinman, R. Hickman, K. Reymann, T. B. McHugh, and V. Vanhoucke, “Google scanned objects: A high-quality dataset of 3d scanned household items,” in 2022 International Conference on Robotics and Automation (ICRA) . IEEE, 2022, pp. 2553–2560

2022

Earlier work this paper cites.

T. Wang, B. Zhang, T. Zhang, S. Gu, J. Bao, T. Baltrusaitis, J. Shen, D. Chen, F. Wen, Q. Chen et al. , “Rodin: A generative model for sculpting 3d digital avatars using diffusion,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 4563–4573

2023

Cited alongside, same era.

H. Zhang, B. Chen, H. Yang, L. Qu, X. Wang, L. Chen, C. Long, F. Zhu, K. Du, and M. Zheng, “Avatarverse: High-quality & stable 3d avatar creation from text and pose,” arXiv preprint arXiv:2308.03610 , 2023

Original

2023

Cited alongside, same era.

C.-H. Lin, J. Gao, L. Tang, T. Takikawa, X. Zeng, X. Huang, K. Kreis, S. Fidler, M.-Y. Liu, and T.-Y. Lin, “Magic3d: High-resolution text-to-3d content creation,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2023

2023

Cited alongside, same era.

Z. Wang, C. Lu, Y. Wang, F. Bao, C. Li, H. Su, and J. Zhu, “Prolificdreamer: High-fidelity and diverse text-to-3d generation with variational score distillation,” arXiv preprint arXiv:2305.16213 , 2023

Original

A. Raj, S. Kaza, B. Poole, M. Niemeyer, N. Ruiz, B. Mildenhall, S. Zada, K. Aberman, M. Rubinstein, J. Barron et al. , “Dreambooth3d: Subject-driven text-to-3d generation,” arXiv preprint arXiv:2303.13508 , 2023

Original

2023

Later among the works it cites.

J. Sun, X. Wang, L. Wang, X. Li, Y. Zhang, H. Zhang, and Y. Liu, “Next3d: Generative neural texture rasterization for 3d-aware head avatars,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2023, pp. 20 991–21 002

2023

Later among the works it cites.

W. Yu, X. Qian, J. Huo, T. Huang, B. Zhao, and Y. Fu, “Pushing the limits of 3d shape generation at scale,” arXiv preprint arXiv:2306.11510 , 2023

Original

2023

Later among the works it cites.

Y.-C. Cheng, H.-Y. Lee, S. Tulyakov, A. G. Schwing, and L.-Y. Gui, “Sdfusion: Multimodal 3d shape completion, reconstruction, and generation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 4456–4465