Fetching the paper…
Reading the bibliography…
We present Emu Video, a text-to-video generation model that factorizes the generation into two steps: first generating an image conditioned on the text, and then generating a video conditioned on the text and the generated image.
Fleiss, J.L., Cohen, J.: The equivalence of weighted kappa and the intraclass correlation coefficient as measures of reliability. Educational and psychological measurement
1973
Earlier work this paper cites.
Laptev, I., Lindeberg, T.: Space-time interest points. In: ICCV (2003)
2003
Earlier work this paper cites.
Song, J., Meng, C., Ermon, S.: Denoising diffusion implicit models. arXiv:2010.02502 (October 2020),
2010
Earlier work this paper cites.
Soomro, K., Zamir, A.R., Shah, M.: UCF101: A dataset of 101 human action classes from videos in the wild. CRCV-TR-12-01 (2012)
2012
Earlier work this paper cites.
2014
Earlier work this paper cites.
Shi, X., Chen, Z., Wang, H., Yeung, D.Y., Wong, W.k., WOO, W.c.: Convolutional lstm network: A machine learning approach for precipitation nowcasting. In: Cortes, C., Lawrence, N., Lee, D., Sugiyama, M., Garnett, R. (eds.) NeurIPS. vol. 28. Curran Associates, Inc. (2015),
2015
Earlier work this paper cites.
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 2256–2265. PMLR, Lille, France (07–09 Jul 2015),
2015
Earlier work this paper cites.
Donahue, J., Krahenbühl, P., Darrell, T.: Adversarial feature learning. In: ICLR (2016)
2016
Earlier work this paper cites.
Finn, C., Goodfellow, I., Levine, S.: Unsupervised learning for physical interaction through video prediction. In: Proceedings of the 30th International Conference on Neural Information Processing Systems. p. 64–72. NIPS’16, Curran Associates Inc., Red Hook, NY, USA (2016)
2016
Earlier work this paper cites.
Mathieu, M., Couprie, C., LeCun, Y.: Deep multi-scale video prediction beyond mean square error (2016)
2016
Earlier work this paper cites.
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., Chen, X.: Improved techniques for training gans. NeurIPS
2016
Earlier work this paper cites.
Vondrick, C., Pirsiavash, H., Torralba, A.: Generating videos with scene dynamics. In: Lee, D.D., Sugiyama, M., von Luxburg, U., Guyon, I., Garnett, R. (eds.) Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain. pp. 613–621 (2016),
2016
Earlier work this paper cites.
Kalchbrenner, N., van den Oord, A., Simonyan, K., Danihelka, I., Vinyals, O., Graves, A., Kavukcuoglu, K.: Video pixel networks. In: Precup, D., Teh, Y.W. (eds.) Proceedings of the 34th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 70, pp. 1771–1779. PMLR (06–11 Aug 2017),
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
Babaeizadeh, M., Finn, C., Erhan, D., Campbell, R.H., Levine, S.: Stochastic variational video prediction. In: ICLR (2018),
2018
Earlier work this paper cites.
Denton, E., Fergus, R.: Stochastic video generation with a learned prior. In: Dy, J., Krause, A. (eds.) Proceedings of the 35th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 80, pp. 1174–1183. PMLR (10–15 Jul 2018),
2018
Earlier work this paper cites.
Wichers, N., Villegas, R., Erhan, D., Lee, H.: Hierarchical long-term video prediction without supervision. In: International Conference on Machine Learning (2018),
2018
Earlier work this paper cites.
Brock, A., Donahue, J., Simonyan, K.: Large scale GAN training for high fidelity natural image synthesis. In: International Conference on Learning Representations (2019),
2019
Earlier work this paper cites.
Clark, A., Donahue, J., Simonyan, K.: Adversarial video generation on complex datasets (2019)
2019
Earlier work this paper cites.
Kim, T., Ahn, S., Bengio, Y.: Variational Temporal Abstraction. Curran Associates Inc., Red Hook, NY, USA (2019)
2019
Earlier work this paper cites.
Unterthiner, T., van Steenkiste, S., Kurach, K., Marinier, R., Michalski, M., Gelly, S.: Fvd: A new metric for video generation (2019)
2019
Earlier work this paper cites.
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
Kumar, M., Babaeizadeh, M., Erhan, D., Finn, C., Levine, S., Dinh, L., Kingma, D.: Videoflow: A conditional flow-based model for stochastic video generation. In: ICLR (2020),
2020
Earlier work this paper cites.
Dhariwal, P., Nichol, A.: Diffusion models beat gans on image synthesis (2021)
2021
Earlier work this paper cites.
Esser, P., Rombach, R., Ommer, B.: Taming transformers for high-resolution image synthesis. In: CVPR (2021)
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
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 (2021)
2021
Earlier work this paper cites.
Ramesh, A., Pavlov, M., Goh, G., Gray, S., Voss, C., Radford, A., Chen, M., Sutskever, I.: Zero-shot text-to-image generation (2021)
2021
Cited alongside, same era.
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models (2021)
2021
Cited alongside, same era.
2021
Cited alongside, same era.
Yan, W., Zhang, Y., Abbeel, P., Srinivas, A.: Videogpt: Video generation using vq-vae and transformers (2021)
2021
Cited alongside, same era.
2023
Closest in time.
Esser, P., Chiu, J., Atighehchian, P., Granskog, J., Germanidis, A.: Structure and content-guided video synthesis with diffusion models (2023)
2023
Closest in time.
Fei, H., Wu, S., Ji, W., Zhang, H., Chua, T.S.: Empowering dynamics-aware text-to-video diffusion with large language models (2023)
2023
Closest in time.
Fu, T.J., Yu, L., Zhang, N., Fu, C.Y., Su, J.C., Wang, W.Y., Bell, S.: Tell me what happened: Unifying text-guided video completion via multimodal masked video generation. In: CVPR. pp. 10681–10692 (June 2023)
2023
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2022
Cited alongside, same era.
Aldausari, N., Sowmya, A., Marcus, N., Mohammadi, G.: Video generative adversarial networks: A review. ACM Comput. Surv
2022
Cited alongside, same era.
Brooks, T., Hellsten, J., Aittala, M., Wang, T.C., Aila, T., Lehtinen, J., Liu, M.Y., Efros, A.A., Karras, T.: Generating long videos of dynamic scenes. In: NeurIPS (2022)
2022
Cited alongside, same era.
2022
Cited alongside, same era.
Ding, M., Zheng, W., Hong, W., Tang, J.: Cogview2: Faster and better text-to-image generation via hierarchical transformers. NeurIPS (2022)
2022
Cited alongside, same era.
2022
Cited alongside, same era.
Gafni, O., Polyak, A., Ashual, O., Sheynin, S., Parikh, D., Taigman, Y.: Make-a-scene: Scene-based text-to-image generation with human priors. In: European Conference on Computer Vision (2022)
2022
Cited alongside, same era.
Harvey, W., Naderiparizi, S., Masrani, V., Weilbach, C., Wood, F.: Flexible diffusion modeling of long videos. In: Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K., Oh, A. (eds.) NeurIPS. vol. 35, pp. 27953–27965. Curran Associates, Inc. (2022),
2022
Cited alongside, same era.
2023
Closest in time.
Gu, J., Wang, S., Zhao, H., Lu, T., Zhang, X., Wu, Z., Xu, S., Zhang, W., Jiang, Y.G., Xu, H.: Reuse and diffuse: Iterative denoising for text-to-video generation (2023)
2023
Closest in time.
Gupta, A., Tian, S., Zhang, Y., Wu, J., Martín-Martín, R., Fei-Fei, L.: Maskvit: Masked visual pre-training for video prediction. In: ICLR (2023),
2023
Closest in time.
He, Y., Yang, T., Zhang, Y., Shan, Y., Chen, Q.: Latent video diffusion models for high-fidelity long video generation (2023)
2023
Closest in time.
Hong, S., Seo, J., Hong, S., Shin, H., Kim, S.: Large language models are frame-level directors for zero-shot text-to-video generation (2023)
2023
Closest in time.
Kang, M., Zhu, J.Y., Zhang, R., Park, J., Shechtman, E., Paris, S., Park, T.: Scaling up gans for text-to-image synthesis. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2023)
2023
Closest in time.
2023
Closest in time.
Lee, S., Kong, C., Jeon, D., Kwak, N.: Aadiff: Audio-aligned video synthesis with text-to-image diffusion (2023)
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
Singer, U., Polyak, A., Hayes, T., Yin, X., An, J., Zhang, S., Hu, Q., Yang, H., Ashual, O., Gafni, O., Parikh, D., Gupta, S., Taigman, Y.: Make-a-video: Text-to-video generation without text-video data. In: ICLR (2023),
2023
Closest in time.
Tang, Z., Yang, Z., Zhu, C., Zeng, M., Bansal, M.: Any-to-any generation via composable diffusion (2023)
2023
Closest in time.
Villegas, R., Babaeizadeh, M., Kindermans, P.J., Moraldo, H., Zhang, H., Saffar, M.T., Castro, S., Kunze, J., Erhan, D.: Phenaki: Variable length video generation from open domain textual descriptions. In: International Conference on Learning Representations (2023),
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
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: ICCV (2023)
2023
Closest in time.
Xing, Z., Dai, Q., Hu, H., Wu, Z., Jiang, Y.G.: Simda: Simple diffusion adapter for efficient video generation (2023)
2023
Closest in time.
2023
Closest in time.
Yin, S., Wu, C., Yang, H., Wang, J., Wang, X., Ni, M., Yang, Z., Li, L., Liu, S., Yang, F., Fu, J., Ming, G., Wang, L., Liu, Z., Li, H., Duan, N.: Nuwa-xl: Diffusion over diffusion for extremely long video generation (2023)
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
Zhang, Y., Wei, Y., Jiang, D., Zhang, X., Zuo, W., Tian, Q.: Controlvideo: Training-free controllable text-to-video generation (2023)
2023
Closest in time.
Zhou, D., Wang, W., Yan, H., Lv, W., Zhu, Y., Feng, J.: Magicvideo: Efficient video generation with latent diffusion models (2023)
2023
Closest in time.