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Diffusion models have revolutionized text-driven video editing.
Wang, Z., Bovik, A., Sheikh, H., Simoncelli, E.: Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing (2004)
2004
Earlier work this paper cites.
Pont-Tuset, J., Perazzi, F., Caelles, S., Arbeláez, P., Sorkine-Hornung, A., Gool, L.V.: The 2017 davis challenge on video object segmentation. In: arXiv (2018)
2018
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: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2018)
2018
Earlier work this paper cites.
Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. In: Advances in Neural Information Processing Systems (2020)
2020
Earlier work this paper cites.
Mercat, A., Viitanen, M., Vanne, J.: Uvg dataset: 50/120fps 4k sequences for video codec analysis and development. In: Proceedings of the 11th ACM Multimedia Systems Conference (2020)
2020
Earlier work this paper cites.
Sitzmann, V., Martel, J., Bergman, A., Lindell, D., Wetzstein, G.: Implicit neural representations with periodic activation functions. In: Advances in Neural Information Processing Systems (2020)
2020
Earlier work this paper cites.
Chen, H., He, B., Wang, H., Ren, Y., Lim, S.N., Shrivastava, A.: Nerv: Neural representations for videos. In: Advances in Neural Information Processing Systems (2021)
2021
Earlier work this paper cites.
Kasten, Y., Ofri, D., Wang, O., Dekel, T.: Layered neural atlases for consistent video editing. ACM Trans. Graph. (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. Commun. ACM (2021)
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., Krueger, G., Sutskever, I.: Learning transferable visual models from natural language supervision. In: Proceedings of the 38th International Conference on Machine Learning (2021)
2021
Earlier work this paper cites.
Song, J., Meng, C., Ermon, S.: Denoising diffusion implicit models. In: International Conference on Learning Representations (2021)
2021
Earlier work this paper cites.
Wang, X., Xie, L., Dong, C., Shan, Y.: Real-esrgan: Training real-world blind super-resolution with pure synthetic data. In: Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops (2021)
2021
Earlier work this paper cites.
Zhang, Y., van Rozendaal, T., Brehmer, J., Nagel, M., Cohen, T.: Implicit neural video compression. In: arXiv (2021)
2021
Earlier work this paper cites.
Bai, Y., Dong, C., Wang, C.: Ps-nerv: Patch-wise stylized neural representations for videos. In: arXiv (2022)
2022
Earlier work this paper cites.
Bar-Tal, O., Ofri-Amar, D., Fridman, R., Kasten, Y., Dekel, T.: Text2live: Text-driven layered image and video editing. In: Computer Vision – ECCV 2022 (2022)
2022
Earlier work this paper cites.
Chan, E.R., Lin, C.Z., Chan, M.A., Nagano, K., Pan, B., De Mello, S., Gallo, O., Guibas, L.J., Tremblay, J., Khamis, S., Karras, T., Wetzstein, G.: Efficient geometry-aware 3d generative adversarial networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2022)
2022
Earlier work this paper cites.
Ho, J., Salimans, T., Gritsenko, A., Chan, W., Norouzi, M., Fleet, D.J.: Video diffusion models. In: arXiv (2022)
2022
Earlier work this paper cites.
Kim, S., Yu, S., Lee, J., Shin, J.: Scalable neural video representations with learnable positional features. In: Advances in Neural Information Processing Systems (2022)
2022
Earlier work this paper cites.
Li, Z., Wang, M., Pi, H., Xu, K., Mei, J., Liu, Y.: E-nerv: Expedite neural video representation with disentangled spatial-temporal context. In: arXiv (2022)
2022
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. In: International Conference on Learning Representations (2022)
2022
Earlier work this paper cites.
Nichol, A.Q., 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. In: Proceedings of the 39th International Conference on Machine Learning (2022)
2022
Cited alongside, same era.
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 (2022)
2022
Cited alongside, same era.
Saharia, C., Chan, W., Saxena, S., Li, L., Whang, J., Denton, E.L., Ghasemipour, K., Gontijo Lopes, R., Karagol Ayan, B., Salimans, T., Ho, J., Fleet, D.J., Norouzi, M.: Photorealistic text-to-image diffusion models with deep language understanding. In: Advances in Neural Information Processing Systems (2022)
2022
Cited alongside, same era.
Wang, Y., Yang, S., Hu, Y., Zhang, J.: Nerfocus: Neural radiance field for 3d synthetic defocus. In: arXiv (2022)
Molad, E., Horwitz, E., Valevski, D., Acha, A.R., Matias, Y., Pritch, Y., Leviathan, Y., Hoshen, Y.: Dreamix: Video diffusion models are general video editors. In: arXiv (2023)
2023
Closest in time.
Mou, C., Wang, X., Xie, L., Wu, Y., Zhang, J., Qi, Z., Shan, Y., Qie, X.: T2i-adapter: Learning adapters to dig out more controllable ability for text-to-image diffusion models. In: arXiv (2023)
2023
Closest in time.
Ouyang, H., Wang, Q., Xiao, Y., Bai, Q., Zhang, J., Zheng, K., Zhou, X., Chen, Q., Shen, Y.: Codef: Content deformation fields for temporally consistent video processing. In: arXiv (2023)
2023
Closest in time.
Podell, D., English, Z., Lacey, K., Blattmann, A., Dockhorn, T., Müller, J., Penna, J., Rombach, R.: Sdxl: Improving latent diffusion models for high-resolution image synthesis. In: arXiv (2023)
2023
Closest in time.
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2022
Cited alongside, same era.
Improving image generation with better captions. https://cdn.openai.com/papers/dall-e-3.pdf (2023)
2023
Cited alongside, same era.
Brooks, T., Holynski, A., Efros, A.A.: Instructpix2pix: Learning to follow image editing instructions. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2023)
2023
Cited alongside, same era.
Ceylan, D., Huang, C.H.P., Mitra, N.J.: Pix2video: Video editing using image diffusion. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (2023)
2023
Cited alongside, same era.
Chai, W., Guo, X., Wang, G., Lu, Y.: Stablevideo: Text-driven consistency-aware diffusion video editing. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (2023)
2023
Cited alongside, same era.
Cheng, X., Wu, Y., Jia, M., Wang, Q., Zhang, J.: Panoptic compositional feature field for editable scene rendering with network-inferred labels via metric learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2023)
2023
Cited alongside, same era.
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. In: arXiv (2023)
2023
Cited alongside, same era.
Couairon, G., Verbeek, J., Schwenk, H., Cord, M.: Diffedit: Diffusion-based semantic image editing with mask guidance. In: The Eleventh International Conference on Learning Representations (2023)
2023
Cited alongside, same era.
Esser, P., Chiu, J., Atighehchian, P., Granskog, J., Germanidis, A.: Structure and content-guided video synthesis with diffusion models. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (2023)
2023
Cited alongside, same era.
Qi, C., Cun, X., Zhang, Y., Lei, C., Wang, X., Shan, Y., Chen, Q.: Fatezero: Fusing attentions for zero-shot text-based video editing. In: arXiv (2023)
2023
Closest in time.
Qin, B., Li, J., Tang, S., Chua, T.S., Zhuang, Y.: Instructvid2vid: Controllable video editing with natural language instructions. In: arXiv (2023)
2023
Closest in time.
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 (2023)
2023
Closest in time.
Shin, C., Kim, H., Lee, C.H., gil Lee, S., Yoon, S.: Edit-a-video: Single video editing with object-aware consistency. In: arXiv (2023)
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: The Eleventh International Conference on Learning Representations (2023)
2023
Closest in time.
Wang, W., Xie, K., Liu, Z., Chen, H., Cao, Y., Wang, X., Shen, C.: Zero-shot video editing using off-the-shelf image diffusion models. In: arXiv (2023)
2023
Closest in time.
Wang, Y., Li, Y., Liu, X., Dai, A., Chan, A., Cui, Z.: Edit temporal-consistent videos with image diffusion model. In: arXiv (2023)
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: Proceedings of the IEEE/CVF International Conference on Computer Vision (2023)
2023
Closest in time.
Yang, S., Zhou, Y., Liu, Z., Loy, C.C.: Rerender a video: Zero-shot text-guided video-to-video translation. In: arXiv (2023)
2023
Closest in time.
Yang, S., Ding, M., Wu, Y., Li, Z., Zhang, J.: Implicit neural representation for cooperative low-light image enhancement. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (2023)
2023
Closest in time.
Yang, S., Zhang, X., Wang, Y., Yu, J., Wang, Y., Zhang, J.: Difflle: Diffusion-guided domain calibration for unsupervised low-light image enhancement. In: arXiv (2023)
2023
Closest in time.
Zhang, L., Rao, A., Agrawala, M.: Adding conditional control to text-to-image diffusion models. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (2023)
2023
Closest in time.
Zhang, X., Li, R., Yu, J., Xu, Y., Li, W., Zhang, J.: Editguard: Versatile image watermarking for tamper localization and copyright protection. In: arXiv (2023)
2023
Closest in time.
Guo, Y., Yang, C., Rao, A., Liang, Z., Wang, Y., Qiao, Y., Agrawala, M., Lin, D., Dai, B.: Animatediff: Animate your personalized text-to-image diffusion models without specific tuning. In: The Twelfth International Conference on Learning Representations (2024)
2024
Closest in time.
Wang, Q., Li, W., Mou, C., Cheng, X., Zhang, J.: 360dvd: Controllable panorama video generation with 360-degree video diffusion model (2024)
2024
Closest in time.