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Text-to-video generative models have made significant strides in recent years, producing high-quality videos that excel in both aesthetic appeal and accurate instruction following, and have become central to digital art creation and user engagement online.
Rule-based machine learning methods for functional prediction
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Neural-symbolic cognitive reasoning
Artur SD’Avila Garcez, Luis C Lamb, and Dov M Gabbay · 2008
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Eder Santana and George Hotz · 2016
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To create what you tell: Generating videos from captions
Yingwei Pan, Zhaofan Qiu, Ting Yao, Houqiang Li, and Tao Mei · 2017
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Maziar Raissi, Paris Perdikaris, and George Em Karniadakis · 2017
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David Ha and Jürgen Schmidhuber · 2018
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Video generation from text
Yitong Li, Martin Min, Dinghan Shen, David Carlson, and Lawrence Carin · 2018
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Conditional gan with discriminative filter generation for text-to-video synthesis
Yogesh Balaji, Martin Renqiang Min, Bing Bai, Rama Chellappa, and Hans Peter Graf · 2019
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fpinns: Fractional physics-informed neural networks
Guofei Pang, Lu Lu, and George Em Karniadakis · 2019
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Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
Maziar Raissi, Paris Perdikaris, and George E Karniadakis · 2019
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Plans: Neuro-symbolic program learning from videos
Raphaël Dang-Nhu · 2020
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Generative adversarial networks
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2020
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Vivit: A video vision transformer
Anurag Arnab, Mostafa Dehghani, Georg Heigold, Chen Sun, Mario Lučić, and Cordelia Schmid · 2021
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Physics-informed neural networks (pinns) for fluid mechanics: A review
Shengze Cai, Zhiping Mao, Zhicheng Wang, Minglang Yin, and George Em Karniadakis · 2021
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A review of possible effects of cognitive biases on interpretation of rule-based machine learning models
Tomáš Kliegr, Štěpán Bahník, and Johannes Fürnkranz · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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VideoCLIP: Contrastive pre-training for zero-shot video-text understanding
Hu Xu, Gargi Ghosh, Po-Yao Huang, Dmytro Okhonko, Armen Aghajanyan, Florian Metze, Luke Zettlemoyer, and Christoph Feichtenhofer · 2021
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Imagen video: High definition video generation with diffusion models, 2022
Jonathan Ho, William Chan, Chitwan Saharia, Jay Whang, Ruiqi Gao, Alexey Gritsenko, Diederik P. Kingma, Ben Poole, Mohammad Norouzi, David J. Fleet, and Tim Salimans · 2022
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Flexible diffusion modeling of long videos
William Harvey, Saeid Naderiparizi, Vaden Masrani, Christian Weilbach, and Frank Wood · 2022
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Video diffusion models
Jonathan Ho, Tim Salimans, Alexey Gritsenko, William Chan, Mohammad Norouzi, and David J Fleet · 2022
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Auto-encoding variational bayes, 2022
Diederik P Kingma and Max Welling · 2022
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Align and prompt: Video-and-language pre-training with entity prompts
Dongxu Li, Junnan Li, Hongdong Li, Juan Carlos Niebles, and Steven CH Hoi · 2022
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Video swin transformer
Ze Liu, Jia Ning, Yue Cao, Yixuan Wei, Zheng Zhang, Stephen Lin, and Han Hu · 2022
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Dreamingv2: Reinforcement learning with discrete world models without reconstruction
Masashi Okada and Tadahiro Taniguchi · 2022
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Stable video diffusion: Scaling latent video diffusion models to large datasets
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Storybench: A multifaceted benchmark for continuous story visualization
Emanuele Bugliarello, H. Hernan Moraldo, Ruben Villegas, Mohammad Babaeizadeh, Mohammad Taghi Saffar, Han Zhang, Dumitru Erhan, Vittorio Ferrari, Pieter-Jan Kindermans, and Paul Voigtlaender · 2023
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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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Evaluation of text-to-video generation models: A dynamics perspective
Mingxiang Liao, Hannan Lu, Xinyu Zhang, Fang Wan, Tianyu Wang, Yuzhong Zhao, Wangmeng Zuo, Qixiang Ye, and Jingdong Wang · 2024
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Towards world simulator: Crafting physical commonsense-based benchmark for video generation
Fanqing Meng, Jiaqi Liao, Xinyu Tan, Wenqi Shao, Quanfeng Lu, Kaipeng Zhang, Yu Cheng, Dianqi Li, Yu Qiao, and Ping Luo · 2024
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Phybench: A physical commonsense benchmark for evaluating text-to-image models
Fanqing Meng, Wenqi Shao, Lixin Luo, Yahong Wang, Yiran Chen, Quanfeng Lu, Yue Yang, Tianshuo Yang, Kaipeng Zhang, Yu Qiao, et al · 2024
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Sora system card, 2024
OpenAI · 2024
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Pika labs 2.2: The future of ai-driven video generation, 2024
Team Pika · 2024
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Learning universal policies via text-guided video generation
Yilun Du, Sherry Yang, Bo Dai, Hanjun Dai, Ofir Nachum, Josh Tenenbaum, Dale Schuurmans, and Pieter Abbeel · 2023
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Structure and content-guided video synthesis with diffusion models
Patrick Esser, Johnathan Chiu, Parmida Atighehchian, Jonathan Granskog, and Anastasis Germanidis · 2023
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Cogvideo: Large-scale pretraining for text-to-video generation via transformers
Wenyi Hong, Ming Ding, Wendi Zheng, Xinghan Liu, and Jie Tang · 2023
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Fetv: A benchmark for fine-grained evaluation of open-domain text-to-video generation
Yuanxin Liu, Lei Li, Shuhuai Ren, Rundong Gao, Shicheng Li, Sishuo Chen, Xu Sun, and Lu Hou · 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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Tune-a-video: One-shot tuning of image diffusion models for text-to-video generation
Jay Zhangjie Wu, Yixiao Ge, Xintao Wang, Stan Weixian Lei, Yuchao Gu, Yufei Shi, Wynne Hsu, Ying Shan, Xiaohu Qie, and Mike Zheng Shou · 2023
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Modelscope text-to-video technical report
Jiuniu Wang, Hangjie Yuan, Dayou Chen, Yingya Zhang, Xiang Wang, and Shiwei Zhang · 2023
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T2v-compbench: A comprehensive benchmark for compositional text-to-video generation
Kaiyue Sun, Kaiyi Huang, Xian Liu, Yue Wu, Zihan Xu, Zhenguo Li, and Xihui Liu · 2024
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Panacea: Panoramic and controllable video generation for autonomous driving
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Probabilistic adaptation of black-box text-to-video models
Sherry Yang, Yilun Du, Bo Dai, Dale Schuurmans, Joshua B. Tenenbaum, and Pieter Abbeel · 2024
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Cogvideox: Text-to-video diffusion models with an expert transformer
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Cogvideox + cogsound, 2024
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Vision language models in autonomous driving: A survey and outlook
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Controlvideo: Training-free controllable text-to-video generation
Yabo Zhang, Yuxiang Wei, Dongsheng Jiang, XIAOPENG ZHANG, Wangmeng Zuo, and Qi Tian · 2024
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Cosmos world foundation model platform for physical ai
Niket Agarwal, Arslan Ali, Maciej Bala, Yogesh Balaji, Erik Barker, Tiffany Cai, Prithvijit Chattopadhyay, Yongxin Chen, Yin Cui, Yifan Ding, et al · 2025
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Wan: Open and advanced large-scale video generative models, 2025
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Videophy: Evaluating physical commonsense for video generation
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Exploring the evolution of physics cognition in video generation: A survey
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Do generative video models learn physical principles from watching videos?
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Hailuo ai advances cinematic storytelling with t2v-01-director and i2v-01-director, 2025
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Wisa: World simulator assistant for physics-aware text-to-video generation
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