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This work aims to learn a high-quality text-to-video (T2V) generative model by leveraging a pre-trained text-to-image (T2I) model as a basis.
Curriculum learning
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Tero Karras, Samuli Laine, and Timo Aila · 2019
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Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu · 2020
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Imaginator: Conditional spatio-temporal gan for video generation
Yaohui WANG, Piotr Bilinski, Francois Bremond, and Antitza Dantcheva · 2020
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G3AN: Disentangling appearance and motion for video generation
Yaohui Wang, Piotr Bilinski, Francois Bremond, and Antitza Dantcheva · 2020
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Motion-based generator model: Unsupervised disentanglement of appearance, trackable and intrackable motions in dynamic patterns
Jianwen Xie, Ruiqi Gao, Zilong Zheng, Song-Chun Zhu, and Ying Nian Wu · 2020
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Shangchen Zhou, Jiawei Zhang, Wangmeng Zuo, and Chen Change Loy · 2020
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Max Bain, Arsha Nagrani, Gül Varol, and Andrew Zisserman · 2021
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The msr-video to text dataset with clean annotations
Haoran Chen, Jianmin Li, Simone Frintrop, and Xiaolin Hu · 2021
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Taming transformers for high-resolution image synthesis
Patrick Esser, Robin Rombach, and Bjorn Ommer · 2021
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Null-text inversion for editing real images using guided diffusion models
Ron Mokady, Amir Hertz, Kfir Aberman, Yael Pritch, and Daniel Cohen-Or · 2022
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Hierarchical text-conditional image generation with clip latents
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen · 2022
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High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
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Photorealistic text-to-image diffusion models with deep language understanding
Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L Denton, Kamyar Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, et al · 2022
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Laion-5b: An open large-scale dataset for training next generation image-text models
Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Wortsman, et al · 2022
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Alexander Quinn Nichol and Prafulla Dhariwal · 2021
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Zero-shot text-to-image generation
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A good image generator is what you need for high-resolution video synthesis
Yu Tian, Jian Ren, Menglei Chai, Kyle Olszewski, Xi Peng, Dimitris N. Metaxas, and Sergey Tulyakov · 2021
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Learning to Generate Human Videos
Yaohui Wang · 2021
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Stylegan-v: A continuous video generator with the price, image quality and perks of stylegan2
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Nüwa: Visual synthesis pre-training for neural visual world creation
Chenfei Wu, Jian Liang, Lei Ji, Fan Yang, Yuejian Fang, Daxin Jiang, and Nan Duan · 2022
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Generating videos with dynamics-aware implicit generative adversarial networks
Sihyun Yu, Jihoon Tack, Sangwoo Mo, Hyunsu Kim, Junho Kim, Jung-Woo Ha, and Jinwoo Shin · 2022
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Towards smooth video composition
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Align your latents: High-resolution video synthesis with latent diffusion models
Andreas Blattmann, Robin Rombach, Huan Ling, Tim Dockhorn, Seung Wook Kim, Sanja Fidler, and Karsten Kreis · 2023
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Videofusion: Decomposed diffusion models for high-quality video generation
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Zero-shot image-to-image translation
Gaurav Parmar, Krishna Kumar Singh, Richard Zhang, Yijun Li, Jingwan Lu, and Jun-Yan Zhu · 2023
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Make-a-video: Text-to-video generation without text-video data
Uriel Singer, Adam Polyak, Thomas Hayes, Xi Yin, Jie An, Songyang Zhang, Qiyuan Hu, Harry Yang, Oron Ashual, Oran Gafni, Devi Parikh, Sonal Gupta, and Yaniv Taigman · 2023
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Llama: Open and efficient foundation language models
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Adding conditional control to text-to-image diffusion models
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