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Large-scale text-to-image (T2I) diffusion models have been extended for text-guided video editing, yielding impressive zero-shot video editing performance.
N. Tumanyan, M. Geyer, S. Bagon, and T. Dekel, “Plug-and-play diffusion features for text-driven image-to-image translation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 1921–1930
1930
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” Advances in neural information processing systems , vol. 27, 2014
2014
Earlier work this paper cites.
O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18 . Springer, 2015, pp. 234–241
2015
Earlier work this paper cites.
E. Mansimov, E. Parisotto, J. L. Ba, and R. Salakhutdinov, “Generating images from captions with attention,” in International Conference on Learning Representations , 2016
2016
Earlier work this paper cites.
S. Reed, Z. Akata, X. Yan, L. Logeswaran, B. Schiele, and H. Lee, “Generative adversarial text to image synthesis,” in International conference on machine learning . PMLR, 2016, pp. 1060–1069
2016
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
A. Creswell, T. White, V. Dumoulin, K. Arulkumaran, B. Sengupta, and A. A. Bharath, “Generative adversarial networks: An overview,” IEEE signal processing magazine , vol. 35, no. 1, pp. 53–65, 2018
2018
Earlier work this paper cites.
D. P. Kingma and P. Dhariwal, “Glow: Generative flow with invertible 1x1 convolutions,” Advances in neural information processing systems , vol. 31, 2018
2018
Earlier work this paper cites.
B. Li, X. Qi, T. Lukasiewicz, and P. Torr, “Controllable text-to-image generation,” Advances in Neural Information Processing Systems , vol. 32, 2019
2019
Earlier work this paper cites.
J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” Advances in neural information processing systems , vol. 33, pp. 6840–6851, 2020
2020
Earlier work this paper cites.
R. Li, N. Wang, F. Feng, G. Zhang, and X. Wang, “Exploring global and local linguistic representations for text-to-image synthesis,” IEEE Transactions on Multimedia , vol. 22, no. 12, pp. 3075–3087, 2020
2020
Earlier work this paper cites.
Y. Song, J. Sohl-Dickstein, D. P. Kingma, A. Kumar, S. Ermon, and B. Poole, “Score-based generative modeling through stochastic differential equations,” in International Conference on Learning Representations , 2021
2021
Earlier work this paper cites.
J. Song, C. Meng, and S. Ermon, “Denoising diffusion implicit models,” in International Conference on Learning Representations , 2021
2021
Earlier work this paper cites.
A. Ramesh, M. Pavlov, G. Goh, S. Gray, C. Voss, A. Radford, M. Chen, and I. Sutskever, “Zero-shot text-to-image generation,” in International Conference on Machine Learning . PMLR, 2021, pp. 8821–8831
2021
Earlier work this paper cites.
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark et al. , “Learning transferable visual models from natural language supervision,” in International conference on machine learning . PMLR, 2021, pp. 8748–8763
2021
Earlier work this paper cites.
C. Meng, Y. He, Y. Song, J. Song, J. Wu, J.-Y. Zhu, and S. Ermon, “Sdedit: Guided image synthesis and editing with stochastic differential equations,” in International Conference on Learning Representations , 2021
2021
Earlier work this paper cites.
Y. Kasten, D. Ofri, O. Wang, and T. Dekel, “Layered neural atlases for consistent video editing,” ACM Transactions on Graphics (TOG) , vol. 40, no. 6, pp. 1–12, 2021
2021
Earlier work this paper cites.
R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-resolution image synthesis with latent diffusion models,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2022, pp. 10 684–10 695
2022
Earlier work this paper cites.
C. Saharia, W. Chan, S. Saxena, L. Li, J. Whang, E. L. Denton, K. Ghasemipour, R. Gontijo Lopes, B. Karagol Ayan, T. Salimans et al. , “Photorealistic text-to-image diffusion models with deep language understanding,” Advances in Neural Information Processing Systems , vol. 35, pp. 36 479–36 494, 2022
2022
Cited alongside, same era.
2022
Cited alongside, same era.
O. Avrahami, D. Lischinski, and O. Fried, “Blended diffusion for text-driven editing of natural images,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 18 208–18 218
2022
Cited alongside, same era.
Q. Cheng, K. Wen, and X. Gu, “Vision-language matching for text-to-image synthesis via generative adversarial networks,” IEEE Transactions on Multimedia , 2022
2022
2023
Closest in time.
J. Z. Wu, Y. Ge, X. Wang, W. Lei, Y. Gu, W. Hsu, Y. Shan, X. Qie, and M. Z. Shou, “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 , 2023
2023
Closest in time.
C. Qi, X. Cun, Y. Zhang, C. Lei, X. Wang, Y. Shan, and Q. Chen, “Fatezero: Fusing attentions for zero-shot text-based video editing,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023
2023
Closest in time.
Y. Wang, Z. Cui, and Y. Li, “Distribution-consistent modal recovering for incomplete multimodal learning,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 22 025–22 034
2023
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A. Q. Nichol, P. Dhariwal, A. Ramesh, P. Shyam, P. Mishkin, B. Mcgrew, I. Sutskever, and M. Chen, “Glide: Towards photorealistic image generation and editing with text-guided diffusion models,” in International Conference on Machine Learning . PMLR, 2022, pp. 16 784–16 804
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Cited alongside, same era.
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O. Bar-Tal, D. Ofri-Amar, R. Fridman, Y. Kasten, and T. Dekel, “Text2live: Text-driven layered image and video editing,” in European Conference on Computer Vision . Springer, 2022, pp. 707–723
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Cited alongside, same era.
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Cited alongside, same era.
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Cited alongside, same era.
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B. Kawar, S. Zada, O. Lang, O. Tov, H. Chang, T. Dekel, I. Mosseri, and M. Irani, “Imagic: Text-based real image editing with diffusion models,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 6007–6017
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W. Chai, X. Guo, G. Wang, and Y. Lu, “Stablevideo: Text-driven consistency-aware diffusion video editing,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 23 040–23 050
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R. Mokady, A. Hertz, K. Aberman, Y. Pritch, and D. Cohen-Or, “Null-text inversion for editing real images using guided diffusion models,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 6038–6047
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