[23] Vivian Paulun, Filipp Schmidt, Jan Assen, and Roland Fleming. Shape, motion, and optical cues to stiffness of elastic objects. Journal of Vision, 17(1):1-22, 01 2017.
[24] Jiajun Wu, Ilker Yildirim, Joseph J Lim, Bill Freeman, and Josh Tenenbaum. Galileo: Perceiving physical object properties by integrating a physics engine with deep learning. Advances in neural information processing systems, 28, 2015.
[25] Mingyu Ding, Zhenfang Chen, Tao Du, Ping Luo, Josh Tenenbaum, and Chuang Gan. Dynamic visual reasoning by learning differentiable physics models from video and language. Advances In Neural Information Processing Systems, 34:887-899, 2021.
[26] Krishna Murthy Jatavallabhula, Miles Macklin, Florian Golemo, Vikram Voleti, Linda Petrini, Martin Weiss, Breandan Considine, Jerome Parent-Levesque, Kevin Xie, Kenny Erleben, Liam Paull, Florian Shkurti, Derek Nowrouzezahrai, and Sanja Fidler. gradsim: Differentiable simulation for system identification and visuomotor control. In ICLR, 2021.
[27] Yunzhu Li, Toru Lin, Kexin Yi, Daniel Bear, Daniel L.K. Yamins, Jiajun Wu, Joshua B. Tenenbaum, and Antonio Torralba. Visual grounding of learned physical models. In International Conference on Machine Learning, 2020.
[28] Kun Wang, Mridul Aanjaneya, and Kostas Bekris. A first principles approach for data-efficient system identification of spring-rod systems via differentiable physics engines. In Learning for Dynamics and Control, pages 651–665. PMLR, 2020.
[29] Bin Wang, Paul Kry, Yuanmin Deng, Uri Ascher, Hui Huang, and Baoquan Chen. Neural material: Learning elastic constitutive material and damping models from sparse data. arXiv preprint arXiv:1808.04931, 2018.
[30] Pulkit Agrawal, Ashvin V Nair, Pieter Abbeel, Jitendra Malik, and Sergey Levine. Learning to poke by poking: Experiential learning of intuitive physics. Advances in neural information processing systems, 29, 2016.
[31] Chelsea Finn and Sergey Levine. Deep visual foresight for planning robot motion. In 2017 IEEE International Conference on Robotics and Automation (ICRA), pages 2786-2793. IEEE, 2017.
[32] Mohammad Babaeizadeh, Mohammad Taghi Saffar, Suraj Nair, Sergey Levine, Chelsea Finn, and Dumitru Erhan. Fitvid: Overfitting in pixel-level video prediction. CoRR, abs/2106.13195, 2021.
[33] Danijar Hafner, Timothy Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee, and James Davidson. Learning latent dynamics for planning from pixels. In International conference on machine learning, pages 2555-2565. PMLR, 2019.
[34] Katerina Fragkiadaki, Pulkit Agrawal, Sergey Levine, and Jitendra Malik. Learning visual predictive models of physics for playing billiards. In Yoshua Bengio and Yann LeCun, editors, 4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, May 2-4, 2016, Conference Track Proceedings, 2016.
[35] Danijar Hafner, Timothy P. Lillicrap, Jimmy Ba, and Mohammad Norouzi. Dream to control: Learning behaviors by latent imagination. CoRR, abs/1912.01603, 2019.
[36] Luis S Piloto, Ari Weinstein, Peter Battaglia, and Matthew Botvinick. Intuitive physics learning in a deep-learning model inspired by developmental psychology. Nature human behaviour, 6(9):1257-1267, 2022.
[37] David Ding, Felix Hill, Adam Santoro, and Matt Botvinick. Object-based attention for spatio-temporal reasoning: Outperforming neuro-symbolic models with flexible distributed architectures. arXiv preprint arXiv:2012.08508, 1, 2020.
[38] Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Hervé Jégou. Training data-efficient image transformers & distillation through attention. In International conference on machine learning, pages 10347-10357. PMLR, 2021.
[39] Vikram Voleti, Alexia Jolicoeur-Martineau, and Christopher Pal. Masked conditional video diffusion for prediction, generation, and interpolation. arXiv preprint arXiv:2205.09853, 2022.
[40] Haoyu Lu, Guoxing Yang, Nanyi Fei, Yuqi Huo, Zhiwu Lu, Ping Luo, and Mingyu Ding. Vdt: An empirical study on video diffusion with transformers. arXiv preprint arXiv:2305.13311, 2023.
[41] Ziyi Wu, Nikita Dvornik, Klaus Greff, Thomas Kipf, and Animesh Garg. Slotformer: Unsupervised visual dynamics simulation with object-centric models. arXiv preprint arXiv:2210.05861, 2022.
[42] Mingyu Ding, Bin Xiao, Noel Codella, Ping Luo, Jingdong Wang, and Lu Yuan. Davit: Dual attention vision transformers. In Computer Vision-ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part XXIV, pages 74-92. Springer, 2022.
[43] Chuang Gan, Jeremy Schwartz, Seth Alter, Martin Schrimpf, James Traer, Julian De Freitas, Jonas Kubilius, Abhishek Bhandwaldar, Nick Haber, Megumi Sano, Kuno Kim, Elias Wang, Damian Mrowca, Michael Lingelbach, Aidan Curtis, Kevin T. Feigelis, Daniel M. Bear, Dan Gutfreund, David D. Cox, James J.