Built on

Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. TIP (2004)
2004
Earlier work this paper cites.
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: International Conference on Machine Learning. pp. 2256–2265. PMLR (2015)
2015
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Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: CVPR (2018)
2018
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.
Eftekhar, A., Sax, A., Malik, J., Zamir, A.: Omnidata: A scalable pipeline for making multi-task mid-level vision datasets from 3d scans. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 10786–10796 (2021)
2021
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Esser, P., Rombach, R., Ommer, B.: Taming transformers for high-resolution image synthesis. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 12873–12883 (2021)
2021
Earlier work this paper cites.
Luo, S., Hu, W.: Diffusion probabilistic models for 3d point cloud generation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 2837–2845 (2021)
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
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Song, J., Meng, C., Ermon, S.: Denoising diffusion implicit models. In: Proceedings of the International Conference on Learning Representations (ICLR) (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. arXiv preprint arXiv:2106.10689 (2021)
Original
2021
Earlier work this paper cites.
Zhou, L., Du, Y., Wu, J.: 3d shape generation and completion through point-voxel diffusion. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 5826–5835 (2021)
2021
Earlier work this paper cites.
Downs, L., Francis, A., Koenig, N., Kinman, B., Hickman, R., Reymann, K., McHugh, T.B., Vanhoucke, V.: Google scanned objects: A high-quality dataset of 3d scanned household items. In: ICRA (2022)
2022
Earlier work this paper cites.
Gao, J., Shen, T., Wang, Z., Chen, W., Yin, K., Li, D., Litany, O., Gojcic, Z., Fidler, S.: Get3d: A generative model of high quality 3d textured shapes learned from images. NeurIPS (2022)
2022
Earlier work this paper cites.
Karras, T., Aittala, M., Aila, T., Laine, S.: Elucidating the design space of diffusion-based generative models. Advances in Neural Information Processing Systems 35
2022
Earlier work this paper cites.
Nichol, A., Jun, H., Dhariwal, P., Mishkin, P., Chen, M.: 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.
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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Watson, D., Chan, W., Martin-Brualla, R., Ho, J., Tagliasacchi, A., Norouzi, M.: Novel view synthesis with diffusion models. arXiv preprint arXiv:2210.04628 (2022)
Original
2022
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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 views. arXiv e-prints pp. arXiv–2211 (2022)
2022
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Zeng, X., Vahdat, A., Williams, F., Gojcic, Z., Litany, O., Fidler, S., Kreis, K.: Lion: Latent point diffusion models for 3d shape generation. In: NeurIPS (2022)
2022
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Anciukevičius, T., Xu, Z., Fisher, M., Henderson, P., Bilen, H., Mitra, N.J., Guerrero, P.: Renderdiffusion: Image diffusion for 3d reconstruction, inpainting and generation. In: CVPR (2023)
2023
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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
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Blattmann, A., Dockhorn, T., Kulal, S., Mendelevitch, D., Kilian, M., Lorenz, D., Levi, Y., English, Z., Voleti, V., Letts, A., et al.: Stable video diffusion: Scaling latent video diffusion models to large datasets. arXiv preprint arXiv:2311.15127 (2023)
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2023
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Chan, E.R., Nagano, K., Chan, M.A., Bergman, A.W., Park, J.J., Levy, A., Aittala, M., De Mello, S., Karras, T., Wetzstein, G.: Generative novel view synthesis with 3d-aware diffusion models. In: ICCV (2023)
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. In: ICCV (2023)
2023
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Chen, Y., Zhang, C., Yang, X., Cai, Z., Yu, G., Yang, L., Lin, G.: It3d: Improved text-to-3d generation with explicit view synthesis. arXiv preprint arXiv:2308.11473 (2023)
Original
2023
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Chen, Z., Wang, F., Liu, H.: Text-to-3d using gaussian splatting. arXiv preprint arXiv:2309.16585 (2023)
Original
2023
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Cheng, X., Yang, T., Wang, J., Li, Y., Zhang, L., Zhang, J., Yuan, L.: Progressive3d: Progressively local editing for text-to-3d content creation with complex semantic prompts. arXiv preprint arXiv:2310.11784 (2023)
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2023
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Cheng, Y.C., Lee, H.Y., Tulyakov, S., Schwing, A.G., Gui, L.Y.: Sdfusion: Multimodal 3d shape completion, reconstruction, and generation. In: CVPR (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: CVPR (2023)
2023
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Deng, C., Jiang, C., Qi, C.R., Yan, X., Zhou, Y., Guibas, L., Anguelov, D., et al.: Nerdi: Single-view nerf synthesis with language-guided diffusion as general image priors. In: CVPR (2023)
2023
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Denninger, M., Winkelbauer, D., Sundermeyer, M., Boerdijk, W., Knauer, M., Strobl, K.H., Humt, M., Triebel, R.: Blenderproc2: A procedural pipeline for photorealistic rendering. Journal of Open Source Software 8
2023
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Erkoç, Z., Ma, F., Shan, Q., Nießner, M., Dai, A.: Hyperdiffusion: Generating implicit neural fields with weight-space diffusion. arXiv preprint arXiv:2303.17015 (2023)
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2023
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Gu, J., Gao, Q., Zhai, S., Chen, B., Liu, L., Susskind, J.: Learning controllable 3d diffusion models from single-view images. arXiv preprint arXiv:2304.06700 (2023)
Original
2023
Earlier work this paper cites.

Similar

Gu, J., Trevithick, A., Lin, K.E., Susskind, J.M., Theobalt, C., Liu, L., Ramamoorthi, R.: Nerfdiff: Single-image view synthesis with nerf-guided distillation from 3d-aware diffusion. In: ICML (2023)
2023
Cited alongside, same era.
Guo, Y.C., Liu, Y.T., Shao, R., Laforte, C., Voleti, V., Luo, G., Chen, C.H., Zou, Z.X., Wang, C., Cao, Y.P., Zhang, S.H.: threestudio: A unified framework for 3d content generation. https://github.com/threestudio-project/threestudio (2023)
2023
Cited alongside, same era.
Gupta, A., Xiong, W., Nie, Y., Jones, I., Oğuz, B.: 3dgen: Triplane latent diffusion for textured mesh generation. arXiv preprint arXiv:2303.05371 (2023)
Original
2023
Cited alongside, same era.
Hong, Y., Zhang, K., Gu, J., Bi, S., Zhou, Y., Liu, D., Liu, F., Sunkavalli, K., Bui, T., Tan, H.: Lrm: Large reconstruction model for single image to 3d. arXiv preprint arXiv:2311.04400 (2023)
Original
2023
Cited alongside, same era.

Then

Shen, Q., Yang, X., Wang, X.: Anything-3d: Towards single-view anything reconstruction in the wild. arXiv preprint arXiv:2304.10261 (2023)
Original
2023
Later among the works it cites.
Shi, R., Chen, H., Zhang, Z., Liu, M., Xu, C., Wei, X., Chen, L., Zeng, C., Su, H.: Zero123++: a single image to consistent multi-view diffusion base model. arXiv preprint arXiv:2310.15110 (2023)
Original
2023
Later among the works it cites.
Shi, Y., Wang, P., Ye, J., Long, M., Li, K., Yang, X.: Mvdream: Multi-view diffusion for 3d generation. arXiv preprint arXiv:2308.16512 (2023)
Original
2023
Later among the works it cites.
Sun, J., Zhang, B., Shao, R., Wang, L., Liu, W., Xie, Z., Liu, Y.: Dreamcraft3d: Hierarchical 3d generation with bootstrapped diffusion prior. arXiv preprint arXiv:2310.16818 (2023)
Original
2023
Later among the works it cites.

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2024

Envision3D: One Image to 3D with Anchor Views Interpolation

Pang, Yatian, Jia, Tanghui, Shi, Yujun et al.

Understand

We present Envision3D, a novel method for efficiently generating high-quality 3D content from a single image.

Recent methods that extract 3D content from multi-view images generated by diffusion models show great potential.
However, it is still challenging for diffusion models to generate dense multi-view consistent images, which is crucial for the quality of 3D content extraction.
To address this issue, we propose a novel cascade diffusion framework, which decomposes the challenging dense views generation task into two tractable stages, namely anchor views generation and anchor views interpolation.

Built on

Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. TIP (2004)
2004
Earlier work this paper cites.
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: International Conference on Machine Learning. pp. 2256–2265. PMLR (2015)
2015
Earlier work this paper cites.
Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: CVPR (2018)
2018
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.
Eftekhar, A., Sax, A., Malik, J., Zamir, A.: Omnidata: A scalable pipeline for making multi-task mid-level vision datasets from 3d scans. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 10786–10796 (2021)
2021
Earlier work this paper cites.
Esser, P., Rombach, R., Ommer, B.: Taming transformers for high-resolution image synthesis. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 12873–12883 (2021)
2021
Earlier work this paper cites.
Luo, S., Hu, W.: Diffusion probabilistic models for 3d point cloud generation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 2837–2845 (2021)
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.
Song, J., Meng, C., Ermon, S.: Denoising diffusion implicit models. In: Proceedings of the International Conference on Learning Representations (ICLR) (2021)
2021
Earlier work this paper cites.
Wang, P., Liu, L., Liu, Y., Theobalt, C., Komura, T., Wang, W.: Neus: Learning neural implicit surfaces by volume rendering for multi-view reconstruction. arXiv preprint arXiv:2106.10689 (2021)
Original
2021
Earlier work this paper cites.
Zhou, L., Du, Y., Wu, J.: 3d shape generation and completion through point-voxel diffusion. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 5826–5835 (2021)
2021
Earlier work this paper cites.
Downs, L., Francis, A., Koenig, N., Kinman, B., Hickman, R., Reymann, K., McHugh, T.B., Vanhoucke, V.: Google scanned objects: A high-quality dataset of 3d scanned household items. In: ICRA (2022)
2022
Earlier work this paper cites.
Gao, J., Shen, T., Wang, Z., Chen, W., Yin, K., Li, D., Litany, O., Gojcic, Z., Fidler, S.: Get3d: A generative model of high quality 3d textured shapes learned from images. NeurIPS (2022)
2022
Earlier work this paper cites.
Karras, T., Aittala, M., Aila, T., Laine, S.: Elucidating the design space of diffusion-based generative models. Advances in Neural Information Processing Systems 35
2022
Earlier work this paper cites.
Nichol, A., Jun, H., Dhariwal, P., Mishkin, P., Chen, M.: 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.
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.
Watson, D., Chan, W., Martin-Brualla, R., Ho, J., Tagliasacchi, A., Norouzi, M.: Novel view synthesis with diffusion models. arXiv preprint arXiv:2210.04628 (2022)
Original
2022
Earlier work this paper cites.
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 views. arXiv e-prints pp. arXiv–2211 (2022)
2022
Earlier work this paper cites.
Zeng, X., Vahdat, A., Williams, F., Gojcic, Z., Litany, O., Fidler, S., Kreis, K.: Lion: Latent point diffusion models for 3d shape generation. In: NeurIPS (2022)
2022
Earlier work this paper cites.
Anciukevičius, T., Xu, Z., Fisher, M., Henderson, P., Bilen, H., Mitra, N.J., Guerrero, P.: Renderdiffusion: Image diffusion for 3d reconstruction, inpainting and generation. In: CVPR (2023)
2023
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.
Blattmann, A., Dockhorn, T., Kulal, S., Mendelevitch, D., Kilian, M., Lorenz, D., Levi, Y., English, Z., Voleti, V., Letts, A., et al.: Stable video diffusion: Scaling latent video diffusion models to large datasets. arXiv preprint arXiv:2311.15127 (2023)
Original
2023
Earlier work this paper cites.
Chan, E.R., Nagano, K., Chan, M.A., Bergman, A.W., Park, J.J., Levy, A., Aittala, M., De Mello, S., Karras, T., Wetzstein, G.: Generative novel view synthesis with 3d-aware diffusion models. In: ICCV (2023)
2023
Earlier work this paper cites.
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. In: ICCV (2023)
2023
Earlier work this paper cites.
Chen, Y., Zhang, C., Yang, X., Cai, Z., Yu, G., Yang, L., Lin, G.: It3d: Improved text-to-3d generation with explicit view synthesis. arXiv preprint arXiv:2308.11473 (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.
Cheng, X., Yang, T., Wang, J., Li, Y., Zhang, L., Zhang, J., Yuan, L.: Progressive3d: Progressively local editing for text-to-3d content creation with complex semantic prompts. arXiv preprint arXiv:2310.11784 (2023)
Original
2023
Earlier work this paper cites.
Cheng, Y.C., Lee, H.Y., Tulyakov, S., Schwing, A.G., Gui, L.Y.: Sdfusion: Multimodal 3d shape completion, reconstruction, and generation. In: CVPR (2023)
2023
Earlier work this paper cites.
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: CVPR (2023)
2023
Earlier work this paper cites.
Deng, C., Jiang, C., Qi, C.R., Yan, X., Zhou, Y., Guibas, L., Anguelov, D., et al.: Nerdi: Single-view nerf synthesis with language-guided diffusion as general image priors. In: CVPR (2023)
2023
Earlier work this paper cites.
Denninger, M., Winkelbauer, D., Sundermeyer, M., Boerdijk, W., Knauer, M., Strobl, K.H., Humt, M., Triebel, R.: Blenderproc2: A procedural pipeline for photorealistic rendering. Journal of Open Source Software 8
2023
Earlier work this paper cites.
Erkoç, Z., Ma, F., Shan, Q., Nießner, M., Dai, A.: Hyperdiffusion: Generating implicit neural fields with weight-space diffusion. arXiv preprint arXiv:2303.17015 (2023)
Original
2023
Earlier work this paper cites.
Gu, J., Gao, Q., Zhai, S., Chen, B., Liu, L., Susskind, J.: Learning controllable 3d diffusion models from single-view images. arXiv preprint arXiv:2304.06700 (2023)
Original
2023
Earlier work this paper cites.

Similar

Gu, J., Trevithick, A., Lin, K.E., Susskind, J.M., Theobalt, C., Liu, L., Ramamoorthi, R.: Nerfdiff: Single-image view synthesis with nerf-guided distillation from 3d-aware diffusion. In: ICML (2023)
2023
Cited alongside, same era.
Guo, Y.C., Liu, Y.T., Shao, R., Laforte, C., Voleti, V., Luo, G., Chen, C.H., Zou, Z.X., Wang, C., Cao, Y.P., Zhang, S.H.: threestudio: A unified framework for 3d content generation. https://github.com/threestudio-project/threestudio (2023)
2023
Cited alongside, same era.
Gupta, A., Xiong, W., Nie, Y., Jones, I., Oğuz, B.: 3dgen: Triplane latent diffusion for textured mesh generation. arXiv preprint arXiv:2303.05371 (2023)
Original
2023
Cited alongside, same era.
Hong, Y., Zhang, K., Gu, J., Bi, S., Zhou, Y., Liu, D., Liu, F., Sunkavalli, K., Bui, T., Tan, H.: Lrm: Large reconstruction model for single image to 3d. arXiv preprint arXiv:2311.04400 (2023)
Original
2023
Cited alongside, same era.

Then

Shen, Q., Yang, X., Wang, X.: Anything-3d: Towards single-view anything reconstruction in the wild. arXiv preprint arXiv:2304.10261 (2023)
Original
2023
Later among the works it cites.
Shi, R., Chen, H., Zhang, Z., Liu, M., Xu, C., Wei, X., Chen, L., Zeng, C., Su, H.: Zero123++: a single image to consistent multi-view diffusion base model. arXiv preprint arXiv:2310.15110 (2023)
Original
2023
Later among the works it cites.
Shi, Y., Wang, P., Ye, J., Long, M., Li, K., Yang, X.: Mvdream: Multi-view diffusion for 3d generation. arXiv preprint arXiv:2308.16512 (2023)
Original
2023
Later among the works it cites.
Sun, J., Zhang, B., Shao, R., Wang, L., Liu, W., Xie, Z., Liu, Y.: Dreamcraft3d: Hierarchical 3d generation with bootstrapped diffusion prior. arXiv preprint arXiv:2310.16818 (2023)
Original
2023
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

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Huang, Y., Wang, J., Shi, Y., Qi, X., Zha, Z.J., Zhang, L.: Dreamtime: An improved optimization strategy for text-to-3d content creation. arXiv preprint arXiv:2306.12422 (2023)

Original

2023

Cited alongside, same era.

Jang, W., Agapito, L.: Nvist: In the wild new view synthesis from a single image with transformers. arXiv preprint arXiv:2312.08568 (2023)

Original

2023

Cited alongside, same era.

Jun, H., Nichol, A.: Shap-e: Generating conditional 3d implicit functions. arXiv preprint arXiv:2305.02463 (2023)

Original

2023

Cited alongside, same era.

Karnewar, A., Mitra, N.J., Vedaldi, A., Novotny, D.: Holofusion: Towards photo-realistic 3d generative modeling. In: ICCV (2023)

2023

Cited alongside, same era.

Kerbl, B., Kopanas, G., Leimkühler, T., Drettakis, G.: 3d gaussian splatting for real-time radiance field rendering. ACM Transactions on Graphics 42

2023

Cited alongside, same era.

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. In: CVPR (2023)

2023

Cited alongside, same era.

Kwak, J.g., Dong, E., Jin, Y., Ko, H., Mahajan, S., Yi, K.M.: Vivid-1-to-3: Novel view synthesis with video diffusion models. arXiv preprint arXiv:2312.01305 (2023)

Original

2023

Cited alongside, same era.

Li, J., Tan, H., Zhang, K., Xu, Z., Luan, F., Xu, Y., Hong, Y., Sunkavalli, K., Shakhnarovich, G., Bi, S.: Instant3d: Fast text-to-3d with sparse-view generation and large reconstruction model. arXiv preprint arXiv:2311.06214 (2023)

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Szymanowicz, S., Rupprecht, C., Vedaldi, A.: Viewset diffusion:(0-) image-conditioned 3d generative models from 2d data. arXiv preprint arXiv:2306.07881 (2023)

Original

2023