Built on

Rajani, N.F., McCann, B., Xiong, C., Socher, R.: Explain yourself! leveraging language models for commonsense reasoning. arXiv preprint arXiv:1906.02361 (2019)
Original
2019
Earlier work this paper cites.
Tancik, M., Srinivasan, P., Mildenhall, B., Fridovich-Keil, S., Raghavan, N., Singhal, U., Ramamoorthi, R., Barron, J., Ng, R.: Fourier features let networks learn high frequency functions in low dimensional domains. Advances in Neural Information Processing Systems
2020
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
2021
Earlier work this paper cites.
Nichol, A., Dhariwal, P., Ramesh, A., Shyam, P., Mishkin, P., McGrew, B., Sutskever, I., Chen, M.: Glide: Towards photorealistic image generation and editing with text-guided diffusion models. arXiv preprint arXiv:2112.10741 (2021)
Original
2021
Earlier work this paper cites.
Pumarola, A., Corona, E., Pons-Moll, G., Moreno-Noguer, F.: D-nerf: Neural radiance fields for dynamic scenes. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 10318–10327 (2021)
2021
Earlier work this paper cites.
Chen, M., Du, J., Pasunuru, R., Mihaylov, T., Iyer, S., Stoyanov, V., Kozareva, Z.: Improving in-context few-shot learning via self-supervised training. arXiv preprint arXiv:2205.01703 (2022)
Original
2022
Earlier work this paper cites.
Huang, Z., Zhang, T., Heng, W., Shi, B., Zhou, S.: Real-time intermediate flow estimation for video frame interpolation. In: European Conference on Computer Vision. pp. 624–642. Springer (2022)
2022
Earlier work this paper cites.
Ichter, B., Brohan, A., Chebotar, Y., Finn, C., Hausman, K., Herzog, A., Ho, D., Ibarz, J., Irpan, A., Jang, E., Julian, R., Kalashnikov, D., Levine, S., Lu, Y., Parada, C., Rao, K., Sermanet, P., Toshev, A., Vanhoucke, V., Xia, F., Xiao, T., Xu, P., Yan, M., Brown, N., Ahn, M., Cortes, O., Sievers, N., Tan, C., Xu, S., Reyes, D., Rettinghouse, J., Quiambao, J., Pastor, P., Luu, L., Lee, K., Kuang, Y., Jesmonth, S., Joshi, N.J., Jeffrey, K., Ruano, R.J., Hsu, J., Gopalakrishnan, K., David, B., Zeng, A., Fu, C.K.: Do as I can, not as I say: Grounding language in robotic affordances. In: Conference on Robot Learning. Proceedings of Machine Learning Research, vol. 205, pp. 287–318. PLMR, Auckland, New Zealand (2022)
2022
Earlier work this paper cites.
Li, L.H., Zhang, P., Zhang, H., Yang, J., Li, C., Zhong, Y., Wang, L., Yuan, L., Zhang, L., Hwang, J., Chang, K., Gao, J.: Grounded language-image pre-training. In: Conference on Computer Vision and Pattern Recognition. pp. 10955–10965. IEEE, New Orleans, LA, USA (2022)
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.
Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., Chen, M.: Hierarchical text-conditional image generation with clip latents. arXiv preprint arXiv:2204.06125 (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.
Saharia, C., Chan, W., Saxena, S., Li, L., Whang, J., Denton, E., Ghasemipour, S.K.S., Gontijo-Lopes, R., Ayan, B.K., Salimans, T., Ho, J., Fleet, D.J., Norouzi, M.: Photorealistic text-to-image diffusion models with deep language understanding. In: Oh, A.H., Agarwal, A., Belgrave, D., Cho, K. (eds.) Advances in Neural Information Processing Systems (2022),
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
Original
2022
Earlier work this paper cites.
Zeroscope
2023
Earlier work this paper cites.
Bahmani, S., Skorokhodov, I., Rong, V., Wetzstein, G., Guibas, L., Wonka, P., Tulyakov, S., Park, J.J., Tagliasacchi, A., Lindell, D.B.: 4d-fy: Text-to-4d generation using hybrid score distillation sampling. arXiv preprint arXiv:2311.17984 (2023)
Original
2023
Earlier work this paper cites.
Bubeck, S., Chandrasekaran, V., Eldan, R., Gehrke, J., Horvitz, E., Kamar, E., Lee, P., Lee, Y.T., Li, Y., Lundberg, S., et al.: Sparks of artificial general intelligence: Early experiments with gpt-4. arXiv preprint arXiv:2303.12712 (2023)
Original
2023
Earlier work this paper cites.

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Bucker, A., Figueredo, L., Haddadin, S., Kapoor, A., Ma, S., Vemprala, S., Bonatti, R.: Latte: Language trajectory transformer. In: 2023 IEEE International Conference on Robotics and Automation (ICRA). pp. 7287–7294. IEEE (2023)
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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. arXiv preprint arXiv:2307.05663 (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
Cited alongside, same era.
Jiang, Y., Zhang, L., Gao, J., Hu, W., Yao, Y.: Consistent4d: Consistent 360
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2023

Then

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)
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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.
Singer, U., Sheynin, S., Polyak, A., Ashual, O., Makarov, I., Kokkinos, F., Goyal, N., Vedaldi, A., Parikh, D., Johnson, J., et al.: Text-to-4d dynamic scene generation. arXiv preprint arXiv:2301.11280 (2023)
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2023
Later among the works it cites.
Tikayat Ray, A., Bhat, A.P., White, R.T., Nguyen, V.M., Pinon Fischer, O.J., Mavris, D.N.: Examining the potential of generative language models for aviation safety analysis: Case study and insights using the aviation safety reporting system (asrs). Aerospace
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Later among the works it cites.

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2024

Comp4D: LLM-Guided Compositional 4D Scene Generation

Xu, Dejia, Liang, Hanwen, Bhatt, Neel P. et al.

Understand

Recent advancements in diffusion models for 2D and 3D content creation have sparked a surge of interest in generating 4D content.

However, the scarcity of 3D scene datasets constrains current methodologies to primarily object-centric generation.
To overcome this limitation, we present Comp4D, a novel framework for Compositional 4D Generation.
Unlike conventional methods that generate a singular 4D representation of the entire scene, Comp4D innovatively constructs each 4D object within the scene separately.

Built on

Rajani, N.F., McCann, B., Xiong, C., Socher, R.: Explain yourself! leveraging language models for commonsense reasoning. arXiv preprint arXiv:1906.02361 (2019)
Original
2019
Earlier work this paper cites.
Tancik, M., Srinivasan, P., Mildenhall, B., Fridovich-Keil, S., Raghavan, N., Singhal, U., Ramamoorthi, R., Barron, J., Ng, R.: Fourier features let networks learn high frequency functions in low dimensional domains. Advances in Neural Information Processing Systems
2020
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
2021
Earlier work this paper cites.
Nichol, A., Dhariwal, P., Ramesh, A., Shyam, P., Mishkin, P., McGrew, B., Sutskever, I., Chen, M.: Glide: Towards photorealistic image generation and editing with text-guided diffusion models. arXiv preprint arXiv:2112.10741 (2021)
Original
2021
Earlier work this paper cites.
Pumarola, A., Corona, E., Pons-Moll, G., Moreno-Noguer, F.: D-nerf: Neural radiance fields for dynamic scenes. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 10318–10327 (2021)
2021
Earlier work this paper cites.
Chen, M., Du, J., Pasunuru, R., Mihaylov, T., Iyer, S., Stoyanov, V., Kozareva, Z.: Improving in-context few-shot learning via self-supervised training. arXiv preprint arXiv:2205.01703 (2022)
Original
2022
Earlier work this paper cites.
Huang, Z., Zhang, T., Heng, W., Shi, B., Zhou, S.: Real-time intermediate flow estimation for video frame interpolation. In: European Conference on Computer Vision. pp. 624–642. Springer (2022)
2022
Earlier work this paper cites.
Ichter, B., Brohan, A., Chebotar, Y., Finn, C., Hausman, K., Herzog, A., Ho, D., Ibarz, J., Irpan, A., Jang, E., Julian, R., Kalashnikov, D., Levine, S., Lu, Y., Parada, C., Rao, K., Sermanet, P., Toshev, A., Vanhoucke, V., Xia, F., Xiao, T., Xu, P., Yan, M., Brown, N., Ahn, M., Cortes, O., Sievers, N., Tan, C., Xu, S., Reyes, D., Rettinghouse, J., Quiambao, J., Pastor, P., Luu, L., Lee, K., Kuang, Y., Jesmonth, S., Joshi, N.J., Jeffrey, K., Ruano, R.J., Hsu, J., Gopalakrishnan, K., David, B., Zeng, A., Fu, C.K.: Do as I can, not as I say: Grounding language in robotic affordances. In: Conference on Robot Learning. Proceedings of Machine Learning Research, vol. 205, pp. 287–318. PLMR, Auckland, New Zealand (2022)
2022
Earlier work this paper cites.
Li, L.H., Zhang, P., Zhang, H., Yang, J., Li, C., Zhong, Y., Wang, L., Yuan, L., Zhang, L., Hwang, J., Chang, K., Gao, J.: Grounded language-image pre-training. In: Conference on Computer Vision and Pattern Recognition. pp. 10955–10965. IEEE, New Orleans, LA, USA (2022)
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.
Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., Chen, M.: Hierarchical text-conditional image generation with clip latents. arXiv preprint arXiv:2204.06125 (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.
Saharia, C., Chan, W., Saxena, S., Li, L., Whang, J., Denton, E., Ghasemipour, S.K.S., Gontijo-Lopes, R., Ayan, B.K., Salimans, T., Ho, J., Fleet, D.J., Norouzi, M.: Photorealistic text-to-image diffusion models with deep language understanding. In: Oh, A.H., Agarwal, A., Belgrave, D., Cho, K. (eds.) Advances in Neural Information Processing Systems (2022),
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
Original
2022
Earlier work this paper cites.
Zeroscope
2023
Earlier work this paper cites.
Bahmani, S., Skorokhodov, I., Rong, V., Wetzstein, G., Guibas, L., Wonka, P., Tulyakov, S., Park, J.J., Tagliasacchi, A., Lindell, D.B.: 4d-fy: Text-to-4d generation using hybrid score distillation sampling. arXiv preprint arXiv:2311.17984 (2023)
Original
2023
Earlier work this paper cites.
Bubeck, S., Chandrasekaran, V., Eldan, R., Gehrke, J., Horvitz, E., Kamar, E., Lee, P., Lee, Y.T., Li, Y., Lundberg, S., et al.: Sparks of artificial general intelligence: Early experiments with gpt-4. arXiv preprint arXiv:2303.12712 (2023)
Original
2023
Earlier work this paper cites.

Similar

Bucker, A., Figueredo, L., Haddadin, S., Kapoor, A., Ma, S., Vemprala, S., Bonatti, R.: Latte: Language trajectory transformer. In: 2023 IEEE International Conference on Robotics and Automation (ICRA). pp. 7287–7294. IEEE (2023)
2023
Cited alongside, same era.
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.
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
Cited alongside, same era.
Jiang, Y., Zhang, L., Gao, J., Hu, W., Yao, Y.: Consistent4d: Consistent 360
Original
2023

Then

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.
Singer, U., Sheynin, S., Polyak, A., Ashual, O., Makarov, I., Kokkinos, F., Goyal, N., Vedaldi, A., Parikh, D., Johnson, J., et al.: Text-to-4d dynamic scene generation. arXiv preprint arXiv:2301.11280 (2023)
Original
2023
Later among the works it cites.
Tikayat Ray, A., Bhat, A.P., White, R.T., Nguyen, V.M., Pinon Fischer, O.J., Mavris, D.N.: Examining the potential of generative language models for aviation safety analysis: Case study and insights using the aviation safety reporting system (asrs). Aerospace
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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Cited alongside, same era.

Katsumata, K., Vo, D.M., Nakayama, H.: An efficient 3d gaussian representation for monocular/multi-view dynamic scenes. arXiv (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 (ToG)

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Kwon, T., Di Palo, N., Johns, E.: Language models as zero-shot trajectory generators. In: 2nd Workshop on Language and Robot Learning: Language as Grounding (2023)

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Li, X., Cao, Z., Sun, H., Zhang, J., Xian, K., Lin, G.: 3d cinemagraphy from a single image. In: CVPR. pp. 4595–4605 (2023)

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Li, Y., Wang, L., Hu, B., Chen, X., Zhong, W., Lyu, C., Zhang, M.: A comprehensive evaluation of gpt-4v on knowledge-intensive visual question answering. arXiv preprint arXiv:2311.07536 (2023)