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We consider the problem of estimating an object's physical properties such as mass, friction, and elasticity directly from video sequences.
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Roozbeh Mottaghi, Hessam Bagherinezhad, Mohammad Rastegari, and Ali Farhadi · 2015
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Galileo: Perceiving physical object properties by integrating a physics engine with deep learning
Jiajun Wu, Ilker Yildirim, Joseph J Lim, William T Freeman, and Joshua B Tenenbaum · 2015
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Ilker Yildirim, Tejas Kulkarni, Winrich Freiwald, and Joshua Tenenbaum · 2015
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Fill and transfer: A simple physics-based approach for containability reasoning
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Pulkit Agrawal, Ashvin Nair, Pieter Abbeel, Jitendra Malik, and Sergey Levine · 2016
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A compositional object-based approach to learning physical dynamics
Michael B. Chang, Tomer Ullman, Antonio Torralba, and Joshua B. Tenenbaum · 2016
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Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, and Pieter Abbeel · 2016
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A differentiable physics engine for deep learning in robotics
Jonas Degrave, Michiel Hermans, Joni Dambre, and Francis Wyffels · 2016
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Katerina Fragkiadaki, Pulkit Agrawal, Sergey Levine, and Jitendra Malik · 2016
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Roozbeh Mottaghi, Mohammad Rastegari, Abhinav Gupta, and Ali Farhadi · 2016
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Matt Pharr, Wenzel Jakob, and Greg Humphreys · 2016
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Danilo Jimenez Rezende, SM Ali Eslami, Shakir Mohamed, Peter Battaglia, Max Jaderberg, and Nicolas Heess · 2016
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Jiajun Wu, Joseph J Lim, Hongyi Zhang, Joshua B Tenenbaum, and William T Freeman · 2016
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Yunzhu Li, Jiajun Wu, Russ Tedrake, Joshua B Tenenbaum, and Antonio Torralba · 2019
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Differentiable cloth simulation for inverse problems
Junbang Liang, Ming Lin, and Vladlen Koltun · 2019
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Towards unsupervised learning of generative models for 3d controllable image synthesis
Yiyi Liao, Katja Schwarz, Lars Mescheder, and Andreas Geiger · 2019
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Charles C Margossian · 2019
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Ilker Yildirim, Michael Janner, Mario Belledonne, Christian Wallraven, W. A. Freiwald, and Joshua B. Tenenbaum · 2017
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Hassan Abu Alhaija, Siva Karthik Mustikovela, Andreas Geiger, and Carsten Rother · 2018
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Mateusz Michalkiewicz, Jhony K. Pontes, Dominic Jack, Mahsa Baktashmotlagh, and Anders Eriksson · 2019
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Ben Mildenhall, Pratul P. Srinivasan, Rodrigo Ortiz-Cayon, Nima Khademi Kalantari, Ravi Ramamoorthi, Ren Ng, and Abhishek Kar · 2019
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Bayessim: adaptive domain randomization via probabilistic inference for robotics simulators
Fabio Ramos, Rafael Carvalhaes Possas, and Dieter Fox · 2019
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Hamiltonian graph networks with ode integrators
Alvaro Sanchez-Gonzalez, Victor Bapst, Kyle Cranmer, and Peter Battaglia · 2019
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EfficientNet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc Le · 2019
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Zhengdao Chen, Jianyu Zhang, Martin Arjovsky, and Léon Bottou · 2020
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Use the Force, Luke! learning to predict physical forces by simulating effects
Kiana Ehsani, Shubham Tulsiani, Saurabh Gupta, Ali Farhadi, and Abhinav Gupta · 2020
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Learning deformable tetrahedral meshes for 3d reconstruction
Jun Gao, Wenzheng Chen, Tommy Xiang, Alec Jacobson, Morgan McGuire, and Sanja Fidler · 2020
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Disentangling physical dynamics from unknown factors for unsupervised video prediction
Vincent Le Guen and Nicolas Thome · 2020
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DiffTaichi: Differentiable programming for physical simulation
Yuanming Hu, Luke Anderson, Tzu-Mao Li, Qi Sun, Nathan Carr, Jonathan Ragan-Kelley, and Frédo Durand · 2020
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Yunzhu Li, Toru Lin, Kexin Yi, Daniel Bear, Daniel L. K. Yamins, Jiajun Wu, Joshua B. Tenenbaum, and Antonio Torralba · 2020
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Differentiable volumetric rendering: Learning implicit 3d representations without 3d supervision
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Tiny Differentiable Simulator , 2020 (accessed May 15, 2020)
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