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Recent years have witnessed the strong power of 3D generation models, which offer a new level of creative flexibility by allowing users to guide the 3D content generation process through a single image or natural language.
2015
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2021
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Luo, S., Hu, W.: Diffusion probabilistic models for 3d point cloud generation (2021)
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
2021
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Deng, C., Jiang, C.M., Qi, C.R., Yan, X., Zhou, Y., Guibas, L., Anguelov, D.: Nerdi: Single-view nerf synthesis with language-guided diffusion as general image priors (2022)
2022
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Gal, R., Alaluf, Y., Atzmon, Y., Patashnik, O., Bermano, A.H., Chechik, G., Cohen-Or, D.: An image is worth one word: Personalizing text-to-image generation using textual inversion (2022)
2022
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Ho, J., Salimans, T.: Classifier-free diffusion guidance. arXiv preprint arXiv:2207.12598 (2022)
2022
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Müller, T., Evans, A., Schied, C., Keller, A.: Instant neural graphics primitives with a multiresolution hash encoding. ACM Transactions on Graphics (ToG) 41
2022
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Nichol, A., Jun, H., Dhariwal, P., Mishkin, P., Chen, M.: Point-e: A system for generating 3d point clouds from complex prompts (2022)
2022
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2022
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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
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2022
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Shue, J.R., Chan, E.R., Po, R., Ankner, Z., Wu, J., Wetzstein, G.: 3d neural field generation using triplane diffusion (2022)
2022
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2023
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2023
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2023
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Chen, H., Gu, J., Chen, A., Tian, W., Tu, Z., Liu, L., Su, H.: Single-stage diffusion nerf: A unified approach to 3d generation and reconstruction (2023)
2023
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Chen, R., Chen, Y., Jiao, N., Jia, K.: Fantasia3d: Disentangling geometry and appearance for high-quality text-to-3d content creation (2023)
2023
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Deitke, M., Schwenk, D., Salvador, J., Weihs, L., Michel, O., VanderBilt, E., Schmidt, L., Ehsani, K., Kembhavi, A., Farhadi, A.: Objaverse: A universe of annotated 3d objects. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 13142–13153 (2023)
2023
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Gupta, A., Xiong, W., Nie, Y., Jones, I., Oğuz, B.: 3dgen: Triplane latent diffusion for textured mesh generation (2023)
2023
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Jiang, Y., Wu, T., Yang, S., Si, C., Lin, D., Qiao, Y., Loy, C.C., Liu, Z.: Videobooth: Diffusion-based video generation with image prompts (2023)
2023
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Kerbl, B., Kopanas, G., Leimkühler, T., Drettakis, G.: 3d gaussian splatting for real-time radiance field rendering. ACM Transactions on Graphics 42
2023
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Kim, S.W., Brown, B., Yin, K., Kreis, K., Schwarz, K., Li, D., Rombach, R., Torralba, A., Fidler, S.: Neuralfield-ldm: Scene generation with hierarchical latent diffusion models (2023)
2023
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Kumari, N., Zhang, B., Zhang, R., Shechtman, E., Zhu, J.Y.: Multi-concept customization of text-to-image diffusion (2023)
2023
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Wu, J.Z., Ge, Y., Wang, X., Lei, S.W., Gu, Y., Shi, Y., Hsu, W., Shan, Y., Qie, X., Shou, M.Z.: Tune-a-video: One-shot tuning of image diffusion models for text-to-video generation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 7623–7633 (2023)
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2023
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2023
Cited alongside, same era.
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: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 300–309 (2023)
2023
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Liu, F., Wu, D., Wei, Y., Rao, Y., Duan, Y.: Sherpa3d: Boosting high-fidelity text-to-3d generation via coarse 3d prior (2023)
2023
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2023
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Lorraine, J., Xie, K., Zeng, X., Lin, C.H., Takikawa, T., Sharp, N., Lin, T.Y., Liu, M.Y., Fidler, S., Lucas, J.: Att3d: Amortized text-to-3d object synthesis (2023)
2023
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Melas-Kyriazi, L., Rupprecht, C., Laina, I., Vedaldi, A.: Realfusion: 360 deg {\deg} reconstruction of any object from a single image (2023)
2023
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Qian, G., Mai, J., Hamdi, A., Ren, J., Siarohin, A., Li, B., Lee, H.Y., Skorokhodov, I., Wonka, P., Tulyakov, S., Ghanem, B.: Magic123: One image to high-quality 3d object generation using both 2d and 3d diffusion priors (2023)
2023
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2023
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Xu, D., Jiang, Y., Wang, P., Fan, Z., Wang, Y., Wang, Z.: Neurallift-360: Lifting an in-the-wild 2d photo to a 3d object with 360 deg {\deg} views (2023)
2023
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Yang, L., Zhang, Z., Song, Y., Hong, S., Xu, R., Zhao, Y., Zhang, W., Cui, B., Yang, M.H.: Diffusion models: A comprehensive survey of methods and applications. ACM Computing Surveys 56
2023
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2023
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2023
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Zhang, B., Tang, J., Niessner, M., Wonka, P.: 3dshape2vecset: A 3d shape representation for neural fields and generative diffusion models (2023)
2023
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2023
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Zhang, C., Zhang, C., Zhang, M., Kweon, I.S.: Text-to-image diffusion models in generative ai: A survey (2023)
2023
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Zhang, J., Tang, Z., Pang, Y., Cheng, X., Jin, P., Wei, Y., Ning, M., Yuan, L.: Repaint123: Fast and high-quality one image to 3d generation with progressive controllable 2d repainting (2023)
2023
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Zhao, Z., Liu, W., Chen, X., Zeng, X., Wang, R., Cheng, P., Fu, B., Chen, T., Yu, G., Gao, S.: Michelangelo: Conditional 3d shape generation based on shape-image-text aligned latent representation (2023)
2023
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Cao, H., Tan, C., Gao, Z., Xu, Y., Chen, G., Heng, P.A., Li, S.Z.: A survey on generative diffusion models. IEEE Transactions on Knowledge and Data Engineering pp. 1–20 (2024). https://doi.org/10.1109/TKDE.2024.3361474
2024
Closest in time.
Chen, W., Hu, H., Li, Y., Ruiz, N., Jia, X., Chang, M.W., Cohen, W.W.: Subject-driven text-to-image generation via apprenticeship learning. Advances in Neural Information Processing Systems 36
2024
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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. Advances in Neural Information Processing Systems 36
2024
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Du, C., Li, Y., Qiu, Z., Xu, C.: Stable diffusion is unstable. Advances in Neural Information Processing Systems 36
2024
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Huang, N., Zhang, T., Yuan, Y., Chen, D., Zhang, S.: Customize-it-3d: High-quality 3d creation from a single image using subject-specific knowledge prior (2024)
2024
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Li, X., Zhang, Q., Kang, D., Cheng, W., Gao, Y., Zhang, J., Liang, Z., Liao, J., Cao, Y.P., Shan, Y.: Advances in 3d generation: A survey (2024)
2024
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2024
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2024
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Ren, Y., Zhou, Y., Yang, J., Shi, J., Liu, D., Liu, F., Kwon, M., Shrivastava, A.: Customize-a-video: One-shot motion customization of text-to-video diffusion models (2024)
2024
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Tang, J., Chen, Z., Chen, X., Wang, T., Zeng, G., Liu, Z.: Lgm: Large multi-view gaussian model for high-resolution 3d content creation (2024)
2024
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