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Lim, J.H., Ye, J.C.: Geometric gan. arXiv preprint arXiv:1705.02894 (2017)
Original
2017
Earlier work this paper cites.
Gal, R., Patashnik, O., Maron, H., Chechik, G., Cohen-Or, D.: Stylegan-nada: Clip-guided domain adaptation of image generators. arXiv preprint arXiv:2108.00946 (2021)
Original
2021
Earlier work this paper cites.
Meng, C., He, Y., Song, Y., Song, J., Wu, J., Zhu, J.Y., Ermon, S.: Sdedit: Guided image synthesis and editing with stochastic differential equations. arXiv preprint arXiv:2108.01073 (2021)
Original
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.
Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A.C., Bengio, Y.: Generative adversarial networks. 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT) pp. 1–7 (2022), https://api.semanticscholar.org/CorpusID:1033682
2022
Earlier work this paper cites.
Hertz, A., Mokady, R., Tenenbaum, J., Aberman, K., Pritch, Y., Cohen-Or, D.: Prompt-to-prompt image editing with cross attention control. arXiv preprint arXiv:2208.01626 (2022)
Original
2022
Earlier work this paper cites.
Hu, E.J., yelong shen, Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: LoRA: Low-rank adaptation of large language models. In: International Conference on Learning Representations (2022), https://openreview.net/forum?id=nZeVKeeFYf9
2022
Earlier work this paper cites.
Brooks, T., Holynski, A., Efros, A.A.: Instructpix2pix: Learning to follow image editing instructions. In: CVPR (2023)
2023
Earlier work this paper cites.
Ceylan, D., Huang, C.H.P., Mitra, N.J.: Pix2video: Video editing using image diffusion. 2023 IEEE/CVF International Conference on Computer Vision (ICCV) pp. 23149–23160 (2023), https://api.semanticscholar.org/CorpusID:257663916
2023
Earlier work this paper cites.
Cheng, J., Xiao, T., He, T.: Consistent video-to-video transfer using synthetic dataset. ArXiv abs/2311.00213
Original
2023
Earlier work this paper cites.
Dai, X., Hou, J., Ma, C.Y., Tsai, S., Wang, J., Wang, R., Zhang, P., Vandenhende, S., Wang, X., Dubey, A., Yu, M., Kadian, A., Radenovic, F., Mahajan, D., Li, K., Zhao, Y., Petrovic, V., Singh, M.K., Motwani, S., Wen, Y., Song, Y., Sumbaly, R., Ramanathan, V., He, Z., Vajda, P., Parikh, D.: Emu: Enhancing image generation models using photogenic needles in a haystack (2023)
2023
Earlier work this paper cites.
Esser, P., Chiu, J., Atighehchian, P., Granskog, J., Germanidis, A.: Structure and content-guided video synthesis with diffusion models. 2023 IEEE/CVF International Conference on Computer Vision (ICCV) pp. 7312–7322 (2023), https://api.semanticscholar.org/CorpusID:256615582
2023
Earlier work this paper cites.
Geyer, M., Bar-Tal, O., Bagon, S., Dekel, T.: Tokenflow: Consistent diffusion features for consistent video editing. ArXiv abs/2307.10373
Original
2023
Earlier work this paper cites.
Girdhar, R., Singh, M., Brown, A., Duval, Q., Azadi, S., Rambhatla, S.S., Shah, A., Yin, X., Parikh, D., Misra, I.: Emu video: Factorizing text-to-video generation by explicit image conditioning (2023)
2023
Earlier work this paper cites.
Hu, L., Gao, X., Zhang, P., Sun, K., Zhang, B., Bo, L.: Animate anyone: Consistent and controllable image-to-video synthesis for character animation. arXiv preprint arXiv:2311.17117 (2023)
Original
2023
Earlier work this paper cites.

Similar

Then

Wang, Y., He, Y., Li, Y., Li, K., Yu, J., Ma, X., Li, X., Chen, G., Chen, X., Wang, Y., et al.: Internvid: A large-scale video-text dataset for multimodal understanding and generation. arXiv preprint arXiv:2307.06942 (2023)
Original
2023
Later among the works it cites.
Wu, B., Chuang, C.Y., Wang, X., Jia, Y., Krishnakumar, K., Xiao, T., Liang, F., Yu, L., Vajda, P.: Fairy: Fast parallelized instruction-guided video-to-video synthesis. ArXiv abs/2312.13834
Original
2023
Later among the works it cites.
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)
2023
Later among the works it cites.
Wu, J.Z., Li, X., Gao, D., Dong, Z., Bai, J., Singh, A., Xiang, X., Li, Y., Huang, Z., Sun, Y., et al.: Cvpr 2023 text guided video editing competition. arXiv preprint arXiv:2310.16003 (2023)
Original

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2024

Video Editing via Factorized Diffusion Distillation

Singer, Uriel, Zohar, Amit, Kirstain, Yuval et al.

Understand

We introduce Emu Video Edit (EVE), a model that establishes a new state-of-the art in video editing without relying on any supervised video editing data.

To develop EVE we separately train an image editing adapter and a video generation adapter, and attach both to the same text-to-image model.
Then, to align the adapters towards video editing we introduce a new unsupervised distillation procedure, Factorized Diffusion Distillation.
This procedure distills knowledge from one or more teachers simultaneously, without any supervised data.

Built on

Lim, J.H., Ye, J.C.: Geometric gan. arXiv preprint arXiv:1705.02894 (2017)
Original
2017
Earlier work this paper cites.
Gal, R., Patashnik, O., Maron, H., Chechik, G., Cohen-Or, D.: Stylegan-nada: Clip-guided domain adaptation of image generators. arXiv preprint arXiv:2108.00946 (2021)
Original
2021
Earlier work this paper cites.
Meng, C., He, Y., Song, Y., Song, J., Wu, J., Zhu, J.Y., Ermon, S.: Sdedit: Guided image synthesis and editing with stochastic differential equations. arXiv preprint arXiv:2108.01073 (2021)
Original
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.
Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A.C., Bengio, Y.: Generative adversarial networks. 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT) pp. 1–7 (2022), https://api.semanticscholar.org/CorpusID:1033682
2022
Earlier work this paper cites.
Hertz, A., Mokady, R., Tenenbaum, J., Aberman, K., Pritch, Y., Cohen-Or, D.: Prompt-to-prompt image editing with cross attention control. arXiv preprint arXiv:2208.01626 (2022)
Original
2022
Earlier work this paper cites.
Hu, E.J., yelong shen, Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: LoRA: Low-rank adaptation of large language models. In: International Conference on Learning Representations (2022), https://openreview.net/forum?id=nZeVKeeFYf9
2022
Earlier work this paper cites.
Brooks, T., Holynski, A., Efros, A.A.: Instructpix2pix: Learning to follow image editing instructions. In: CVPR (2023)
2023
Earlier work this paper cites.
Ceylan, D., Huang, C.H.P., Mitra, N.J.: Pix2video: Video editing using image diffusion. 2023 IEEE/CVF International Conference on Computer Vision (ICCV) pp. 23149–23160 (2023), https://api.semanticscholar.org/CorpusID:257663916
2023
Earlier work this paper cites.
Cheng, J., Xiao, T., He, T.: Consistent video-to-video transfer using synthetic dataset. ArXiv abs/2311.00213
Original
2023
Earlier work this paper cites.
Dai, X., Hou, J., Ma, C.Y., Tsai, S., Wang, J., Wang, R., Zhang, P., Vandenhende, S., Wang, X., Dubey, A., Yu, M., Kadian, A., Radenovic, F., Mahajan, D., Li, K., Zhao, Y., Petrovic, V., Singh, M.K., Motwani, S., Wen, Y., Song, Y., Sumbaly, R., Ramanathan, V., He, Z., Vajda, P., Parikh, D.: Emu: Enhancing image generation models using photogenic needles in a haystack (2023)
2023
Earlier work this paper cites.
Esser, P., Chiu, J., Atighehchian, P., Granskog, J., Germanidis, A.: Structure and content-guided video synthesis with diffusion models. 2023 IEEE/CVF International Conference on Computer Vision (ICCV) pp. 7312–7322 (2023), https://api.semanticscholar.org/CorpusID:256615582
2023
Earlier work this paper cites.
Geyer, M., Bar-Tal, O., Bagon, S., Dekel, T.: Tokenflow: Consistent diffusion features for consistent video editing. ArXiv abs/2307.10373
Original
2023
Earlier work this paper cites.
Girdhar, R., Singh, M., Brown, A., Duval, Q., Azadi, S., Rambhatla, S.S., Shah, A., Yin, X., Parikh, D., Misra, I.: Emu video: Factorizing text-to-video generation by explicit image conditioning (2023)
2023
Earlier work this paper cites.
Hu, L., Gao, X., Zhang, P., Sun, K., Zhang, B., Bo, L.: Animate anyone: Consistent and controllable image-to-video synthesis for character animation. arXiv preprint arXiv:2311.17117 (2023)
Original
2023
Earlier work this paper cites.

Similar

Then

Wang, Y., He, Y., Li, Y., Li, K., Yu, J., Ma, X., Li, X., Chen, G., Chen, X., Wang, Y., et al.: Internvid: A large-scale video-text dataset for multimodal understanding and generation. arXiv preprint arXiv:2307.06942 (2023)
Original
2023
Later among the works it cites.
Wu, B., Chuang, C.Y., Wang, X., Jia, Y., Krishnakumar, K., Xiao, T., Liang, F., Yu, L., Vajda, P.: Fairy: Fast parallelized instruction-guided video-to-video synthesis. ArXiv abs/2312.13834
Original
2023
Later among the works it cites.
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)
2023
Later among the works it cites.
Wu, J.Z., Li, X., Gao, D., Dong, Z., Bai, J., Singh, A., Xiang, X., Li, Y., Huang, Z., Sun, Y., et al.: Cvpr 2023 text guided video editing competition. arXiv preprint arXiv:2310.16003 (2023)
Original

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Ma, H., Mahdizadehaghdam, S., Wu, B., Fan, Z., Gu, Y., Zhao, W., Shapira, L., Xie, X.: Maskint: Video editing via interpolative non-autoregressive masked transformers. arxiv preprint (2023)

2023

Cited alongside, same era.

Meng, C., Rombach, R., Gao, R., Kingma, D., Ermon, S., Ho, J., Salimans, T.: On distillation of guided diffusion models. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 14297–14306 (2023)

2023

Cited alongside, same era.

Oquab, M., Darcet, T., Moutakanni, T., Vo, H., Szafraniec, M., Khalidov, V., Fernandez, P., Haziza, D., Massa, F., El-Nouby, A., et al.: Dinov2: Learning robust visual features without supervision. arXiv preprint arXiv:2304.07193 (2023)

Original

2023

Cited alongside, same era.

Poole, B., Jain, A., Barron, J.T., Mildenhall, B.: Dreamfusion: Text-to-3d using 2d diffusion. In: The Eleventh International Conference on Learning Representations (2023), https://openreview.net/forum?id=FjNys5c7VyY

2023

Cited alongside, same era.

Ruiz, N., Li, Y., Jampani, V., Pritch, Y., Rubinstein, M., Aberman, K.: Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 22500–22510 (2023)

2023

Cited alongside, same era.

Sauer, A., Lorenz, D., Blattmann, A., Rombach, R.: Adversarial diffusion distillation. ArXiv abs/2311.17042

Original

2023

Cited alongside, same era.

Sauer, A., Lorenz, D., Blattmann, A., Rombach, R.: Adversarial diffusion distillation (2023)

2023

Cited alongside, same era.

Sheynin, S., Polyak, A., Singer, U., Kirstain, Y., Zohar, A., Ashual, O., Parikh, D., Taigman, Y.: Emu edit: Precise image editing via recognition and generation tasks (2023)

2023

Cited alongside, same era.

2023