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Customized text-to-video generation aims to generate text-guided videos with user-given subjects, which has gained increasing attention.
N. Kumari, B. Zhang, R. Zhang, E. Shechtman, and J.-Y. Zhu, “Multi-concept customization of text-to-image diffusion,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 1931–1941
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Y. Liu, X. Wang, Y. Yuan, and W. Zhu, “Cross-modal dual learning for sentence-to-video generation,” in Proceedings of the 27th ACM international conference on multimedia , 2019, pp. 1239–1247
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M. Bain, A. Nagrani, G. Varol, and A. Zisserman, “Frozen in time: A joint video and image encoder for end-to-end retrieval,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 1728–1738
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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
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H. Xue, T. Hang, Y. Zeng, Y. Sun, B. Liu, H. Yang, J. Fu, and B. Guo, “Advancing high-resolution video-language representation with large-scale video transcriptions,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 5036–5045
2022
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W. Hong, M. Ding, W. Zheng, X. Liu, and J. Tang, “Cogvideo: Large-scale pretraining for text-to-video generation via transformers,” in The Eleventh International Conference on Learning Representations , 2022
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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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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
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2022
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2022
L. Han, Y. Li, H. Zhang, P. Milanfar, D. Metaxas, and F. Yang, “Svdiff: Compact parameter space for diffusion fine-tuning,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 7323–7334
2023
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Y. Gu, X. Wang, J. Z. Wu, Y. Shi, Y. Chen, Z. Fan, W. Xiao, R. Zhao, S. Chang, W. Wu et al. , “Mix-of-show: Decentralized low-rank adaptation for multi-concept customization of diffusion models,” Advances in Neural Information Processing Systems , vol. 36, pp. 15 890–15 902, 2023
2023
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Y. Wei, Y. Zhang, Z. Ji, J. Bai, L. Zhang, and W. Zuo, “Elite: Encoding visual concepts into textual embeddings for customized text-to-image generation,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 15 943–15 953
2023
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W. Chen, H. Hu, Y. Li, N. Ruiz, X. Jia, M.-W. Chang, and W. W. Cohen, “Subject-driven text-to-image generation via apprenticeship learning,” Advances in Neural Information Processing Systems , vol. 36, pp. 30 286–30 305, 2023
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C. Lu, Y. Zhou, F. Bao, J. Chen, C. Li, and J. Zhu, “Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps,” Advances in Neural Information Processing Systems , vol. 35, pp. 5775–5787, 2022
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W. Feng, X. He, T.-J. Fu, V. Jampani, A. R. Akula, P. Narayana, S. Basu, X. E. Wang, and W. Y. Wang, “Training-free structured diffusion guidance for compositional text-to-image synthesis,” in The Eleventh International Conference on Learning Representations , 2022
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P. V. Platen, S. Patil, A. Lozhkov, P. Cuenca, N. Lambert, K. Rasul, M. Davaadorj, and T. Wolf, “Diffusers: State-of-the-art diffusion models,” https://github.com/huggingface/diffusers , 2022
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U. Singer, A. Polyak, T. Hayes, X. Yin, J. An, S. Zhang, Q. Hu, H. Yang, O. Ashual, O. Gafni et al. , “Make-a-video: Text-to-video generation without text-video data,” in The Eleventh International Conference on Learning Representations , 2023
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2023
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Z. Luo, D. Chen, Y. Zhang, Y. Huang, L. Wang, Y. Shen, D. Zhao, J. Zhou, and T. Tan, “Videofusion: Decomposed diffusion models for high-quality video generation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 10 209–10 218
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2023
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2023
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O. Avrahami, K. Aberman, O. Fried, D. Cohen-Or, and D. Lischinski, “Break-a-scene: Extracting multiple concepts from a single image,” in SIGGRAPH Asia 2023 Conference Papers , 2023, pp. 1–12
2023
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H. Huang, Y. Feng, C. Shi, L. Xu, J. Yu, and S. Yang, “Free-bloom: Zero-shot text-to-video generator with llm director and ldm animator,” Advances in Neural Information Processing Systems , vol. 36, 2024
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Y. Guo, C. Yang, A. Rao, Z. Liang, Y. Wang, Y. Qiao, M. Agrawala, D. Lin, and B. Dai, “Animatediff: Animate your personalized text-to-image diffusion models without specific tuning,” in The Twelfth International Conference on Learning Representations , 2024
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S. Liu, Y. Zhang, W. Li, Z. Lin, and J. Jia, “Video-p2p: Video editing with cross-attention control,” in 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . IEEE, 2024, pp. 8599–8608
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H. Chen, Y. Zhang, S. Wu, X. Wang, X. Duan, Y. Zhou, and W. Zhu, “Disenbooth: Identity-preserving disentangled tuning for subject-driven text-to-image generation,” in The Twelfth International Conference on Learning Representations , 2024
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