[28] M. Ding, Z. Yang, W. Hong, W. Zheng, C. Zhou, D. Yin, J. Lin, X. Zou, Z. Shao, H. Yang, and J. Tang, "Cogview: Mastering text-to-image generation via transformers," in Advances in Neural Information Processing Systems, 2021, pp. 19822-19835.
[29] 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, 2021, pp. 8821–8831.
[30] J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” Advances in Neural Information Processing Systems, vol. 33, pp. 6840–6851, 2020.
[31] 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.
[32] A. Q. Nichol and P. Dhariwal, “Improved denoising diffusion probabilistic models,” in International Conference on Machine Learning, 2021, pp. 8162–8171.
[33] 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, 2021, pp. 8748-8763.
[34] J. Song, C. Meng, and S. Ermon, "Denoising diffusion implicit models," in International Conference on Learning Representations, 2021.
[35] W. Peebles and S. Xie, "Scalable diffusion models with transformers," arXiv preprint arXiv:2212.09748, 2022.
[36] J. Zhang, C. Xu, L. Liu, M. Wang, X. Wu, Y. Liu, and Y. Jiang, "Dtvnet: Dynamic time-lapse video generation via single still image," in European Conference on Computer Vision. Springer, 2020, pp. 300-315.
[37] A. Siarohin, S. Lathuilière, S. Tulyakov, E. Ricci, and N. Sebe, "First order motion model for image animation," Advances in Neural Information Processing Systems, vol. 32, 2019.
[38] K. Soomro, A. R. Zamir, and M. Shah, "UCF101: A dataset of 101 human actions classes from videos in the wild," CoRR, vol. abs/1212.0402, 2012.
[39] M. Bain, A. Nagrani, G. Varol, and A. Zisserman, "Frozen in time: A joint video and image encoder for end-to-end retrieval," in IEEE International Conference on Computer Vision, 2021.
[40] A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga et al., "Pytorch: An imperative style, high-performance deep learning library," Advances in neural information processing systems, vol. 32, 2019.
[41] Y. Tian, J. Ren, M. Chai, K. Olszewski, X. Peng, D. N. Metaxas, and S. Tulyakov, "A good image generator is what you need for high-resolution video synthesis," arXiv preprint arXiv:2104.15069, 2021.
[42] W. Yan, Y. Zhang, P. Abbeel, and A. Srinivas, "Video generation using vq-vae and transformers," arXiv preprint arXiv:2104.10157, 2021.
[43] W. Wang, H. Yang, Z. Tuo, H. He, J. Zhu, J. Fu, and J. Liu, "Videofactory: Swap attention in spatiotemporal diffusions for text-to-video generation," arXiv preprint arXiv:2305.10874, 2023.
[44] Y. He, T. Yang, Y. Zhang, Y. Shan, and Q. Chen, "Latent video diffusion models for high-fidelity video generation with arbitrary lengths," arXiv preprint arXiv:2211.13221, 2022.
[45] T.-J. Fu, L. Yu, N. Zhang, C.-Y. Fu, J.-C. Su, W. Y. Wang, and S. Bell, "Tell me what happened: Unifying text-guided video completion via multimodal masked video generation," arXiv preprint arXiv:2211.12824, 2022.
[46] 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," arXiv preprint arXiv:2209.14792, 2022.
[47] D. Zhou, W. Wang, H. Yan, W. Lv, Y. Zhu, and J. Feng, "Magicvideo: Efficient video generation with latent diffusion models," arXiv preprint arXiv:2211.11018, 2022.