ScaleDreamer: Scalable Text-to-3D Synthesis with Asynchronous Score Distillation

Koster, R.: Theory of fun for game design. " O’Reilly Media, Inc." (2013)

2013

Earlier work this paper cites.

He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 770–778 (2016)

2016

Earlier work this paper cites.

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30

2017

Earlier work this paper cites.

Miyato, T., Kataoka, T., Koyama, M., Yoshida, Y.: Spectral normalization for generative adversarial networks. arXiv preprint arXiv:1802.05957 (2018)

Original

2018

Earlier work this paper cites.

Sitzmann, V., Thies, J., Heide, F., Nießner, M., Wetzstein, G., Zollhofer, M.: Deepvoxels: Learning persistent 3d feature embeddings. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 2437–2446 (2019)

2019

Earlier work this paper cites.

Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. Advances in neural information processing systems 33

2020

Earlier work this paper cites.

Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., Aila, T.: Analyzing and improving the image quality of stylegan. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 8110–8119 (2020)

2020

Earlier work this paper cites.

Song, Y., Sohl-Dickstein, J., Kingma, D.P., Kumar, A., Ermon, S., Poole, B.: Score-based generative modeling through stochastic differential equations. arXiv preprint arXiv:2011.13456 (2020)

Original

2020

Earlier work this paper cites.

Thanh-Tung, H., Tran, T.: Catastrophic forgetting and mode collapse in gans. In: 2020 international joint conference on neural networks (ijcnn). pp. 1–10. IEEE (2020)

2020

Earlier work this paper cites.

Wohlgenannt, I., Simons, A., Stieglitz, S.: Virtual reality. Business & Information Systems Engineering 62

2020

Earlier work this paper cites.

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

Original

2021

Earlier work this paper cites.

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

2021

Earlier work this paper cites.

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

2021

Earlier work this paper cites.

Shen, T., Gao, J., Yin, K., Liu, M.Y., Fidler, S.: Deep marching tetrahedra: a hybrid representation for high-resolution 3d shape synthesis. Advances in Neural Information Processing Systems 34

2021

Earlier work this paper cites.

Yariv, L., Gu, J., Kasten, Y., Lipman, Y.: Volume rendering of neural implicit surfaces. Advances in Neural Information Processing Systems 34

2021

Earlier work this paper cites.

Balaji, Y., Nah, S., Huang, X., Vahdat, A., Song, J., Kreis, K., Aittala, M., Aila, T., Laine, S., Catanzaro, B., et al.: ediffi: Text-to-image diffusion models with an ensemble of expert denoisers. arXiv preprint arXiv:2211.01324 (2022)

Original

2022

Earlier work this paper cites.

Chan, E.R., Lin, C.Z., Chan, M.A., Nagano, K., Pan, B., De Mello, S., Gallo, O., Guibas, L.J., Tremblay, J., Khamis, S., et al.: Efficient geometry-aware 3d generative adversarial networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 16123–16133 (2022)

2022

Earlier work this paper cites.

Ho, J., Salimans, T.: Classifier-free diffusion guidance. arXiv preprint arXiv:2207.12598 (2022)

Original

2022

Earlier work this paper cites.

Müller, T., Evans, A., Schied, C., Keller, A.: Instant neural graphics primitives with a multiresolution hash encoding. ACM Transactions on Graphics (ToG) 41

2022

Earlier work this paper cites.

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

Original

2022

Earlier work this paper cites.

Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 10684–10695 (2022)

2022

Earlier work this paper cites.

Salimans, T., Ho, J.: Progressive distillation for fast sampling of diffusion models. arXiv preprint arXiv:2202.00512 (2022)

Original

2022

Earlier work this paper cites.

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 35

2022

Earlier work this paper cites.

Schwarz, K., Sauer, A., Niemeyer, M., Liao, Y., Geiger, A.: Voxgraf: Fast 3d-aware image synthesis with sparse voxel grids. Advances in Neural Information Processing Systems 35

2022

Earlier work this paper cites.

Armandpour, M., Zheng, H., Sadeghian, A., Sadeghian, A., Zhou, M.: Re-imagine the negative prompt algorithm: Transform 2d diffusion into 3d, alleviate janus problem and beyond. arXiv preprint arXiv:2304.04968 (2023)

Original

2023

Earlier work this paper cites.

Babu, S., Liu, R., Zhou, A., Maire, M., Shakhnarovich, G., Hanocka, R.: Hyperfields: Towards zero-shot generation of nerfs from text. arXiv preprint arXiv:2310.17075 (2023)

Original

2023

Earlier work this paper cites.

Bahmani, S., Park, J.J., Paschalidou, D., Yan, X., Wetzstein, G., Guibas, L., Tagliasacchi, A.: Cc3d: Layout-conditioned generation of compositional 3d scenes. arXiv preprint arXiv:2303.12074 (2023)

Original

2023

Earlier work this paper cites.

Cao, Z., Hong, F., Wu, T., Pan, L., Liu, Z.: Large-vocabulary 3d diffusion model with transformer. arXiv preprint arXiv:2309.07920 (2023)

Original

2023

Earlier work this paper cites.

Chen, R., Chen, Y., Jiao, N., Jia, K.: Fantasia3d: Disentangling geometry and appearance for high-quality text-to-3d content creation. arXiv preprint arXiv:2303.13873 (2023)

Original

2023

Earlier work this paper cites.

Chen, Z., Wang, F., Liu, H.: Text-to-3d using gaussian splatting. arXiv preprint arXiv:2309.16585 (2023)

Original

2023

Earlier work this paper cites.

Deitke, M., Liu, R., Wallingford, M., Ngo, H., Michel, O., Kusupati, A., Fan, A., Laforte, C., Voleti, V., Gadre, S.Y., et al.: Objaverse-xl: A universe of 10m+ 3d objects. arXiv preprint arXiv:2307.05663 (2023)

Original

2023

Cited alongside, same era.

Ding, L., Dong, S., Huang, Z., Wang, Z., Zhang, Y., Gong, K., Xu, D., Xue, T.: Text-to-3d generation with bidirectional diffusion using both 2d and 3d priors. arXiv preprint arXiv:2312.04963 (2023)

Original

2023

Cited alongside, same era.

Guo, P., Hao, H., Caccavale, A., Ren, Z., Zhang, E., Shan, Q., Sankar, A., Schwing, A.G., Colburn, A., Ma, F.: Stabledreamer: Taming noisy score distillation sampling for text-to-3d. arXiv preprint arXiv:2312.02189 (2023)

Original

2023

Cited alongside, same era.

He, Y., Bai, Y., Lin, M., Zhao, W., Hu, Y., Sheng, J., Yi, R., Li, J., Liu, Y.J.: T 3 bench: Benchmarking current progress in text-to-3d generation. arXiv preprint arXiv:2310.02977 (2023)

Original

2023

Cited alongside, same era.

Wu, T., Zhang, J., Fu, X., Wang, Y., Ren, J., Pan, L., Wu, W., Yang, L., Wang, J., Qian, C., et al.: Omniobject3d: Large-vocabulary 3d object dataset for realistic perception, reconstruction and generation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 803–814 (2023)

2023

Later among the works it cites.

Xu, Y., Tan, H., Luan, F., Bi, S., Wang, P., Li, J., Shi, Z., Sunkavalli, K., Wetzstein, G., Xu, Z., et al.: Dmv3d: Denoising multi-view diffusion using 3d large reconstruction model. arXiv preprint arXiv:2311.09217 (2023)

Original

2023

Later among the works it cites.

Yang, Z., Feng, R., Zhang, H., Shen, Y., Zhu, K., Huang, L., Zhang, Y., Liu, Y., Zhao, D., Zhou, J., et al.: Eliminating lipschitz singularities in diffusion models. arXiv preprint arXiv:2306.11251 (2023)

Original

2023

Later among the works it cites.

Yang, Z., Feng, R., Zhang, H., Shen, Y., Zhu, K., Huang, L., Zhang, Y., Liu, Y., Zhao, D., Zhou, J., et al.: Lipschitz singularities in diffusion models. In: The Twelfth International Conference on Learning Representations (2023)