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Humans have a remarkable ability to predict the effect of physical interactions on the dynamics of objects.
The perception of causality in infants
A. M. Leslie · 1982
Earlier work this paper cites.
Is the top object adequately supported by the bottom object? young infants’ understanding of support relations
R. Baillargeon and S. Hanko-Summers · 1990
Earlier work this paper cites.
Simulation as an engine of physical scene understanding
P. W. Battaglia, J. B. Hamrick, and J. B. Tenenbaum · 2013
Earlier work this paper cites.
Unified particle physics for real-time applications
M. Macklin, M. Müller, N. Chentanez, and T.-Y. Kim · 2014
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
Earlier work this paper cites.
Shapenet: An information-rich 3d model repository
A. X. Chang, T. Funkhouser, L. Guibas, P. Hanrahan, Q. Huang, Z. Li, S. Savarese, M. Savva, S. Song, H. Su, et al · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2015
Earlier work this paper cites.
Action-conditional video prediction using deep networks in atari games
J. Oh, X. Guo, H. Lee, R. L. Lewis, and S. P. Singh · 2015
Earlier work this paper cites.
ImageNet Large Scale Visual Recognition Challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei · 2015
Earlier work this paper cites.
Galileo: Perceiving physical object properties by integrating a physics engine with deep learning
J. Wu, I. Yildirim, J. J. Lim, W. T. Freeman, and J. B. Tenenbaum · 2015
Earlier work this paper cites.
3d shapenets: A deep representation for volumetric shapes
Z. Wu, S. Song, A. Khosla, F. Yu, L. Zhang, X. Tang, and J. Xiao · 2015
Earlier work this paper cites.
Learning to poke by poking: Experiential learning of intuitive physics
P. Agrawal, A. Nair, P. Abbeel, J. Malik, and S. Levine · 2016
Earlier work this paper cites.
Interaction networks for learning about objects, relations and physics
P. Battaglia, R. Pascanu, M. Lai, D. J. Rezende, and K. kavukcuoglu · 2016
Earlier work this paper cites.
Unsupervised learning for physical interaction through video prediction
C. Finn, I. Goodfellow, and S. Levine · 2016
Earlier work this paper cites.
Learning visual predictive models of physics for playing billiards
K. Fragkiadaki, P. Agrawal, S. Levine, and J. Malik · 2016
Cited alongside, same era.
Imagination-based decision making with physical models in deep neural networks
J. B. Hamrick, R. Pascanu, O. Vinyals, A. Ballard, N. Heess, and P. Battaglia · 2016
Cited alongside, same era.
Learning physical intuition of block towers by example
A. Lerer, S. Gross, and R. Fergus · 2016
Cited alongside, same era.
To fall or not to fall: A visual approach to physical stability prediction
W. Li, S. Azimi, A. Leonardis, and M. Fritz · 2016
Cited alongside, same era.
Generalizable Features From Unsupervised Learning
M. Mirza, A. Courville, and Y. Bengio · 2016
Cited alongside, same era.
A disentangled recognition and nonlinear dynamics model for unsupervised learning
M. Fraccaro, S. Kamronn, U. Paquet, and O. Winther · 2017
Later among the works it cites.
Visual stability prediction for robotic manipulation
W. Li, A. Leonardis, and M. Fritz · 2017
Later among the works it cites.
Pointnet: Deep learning on point sets for 3d classification and segmentation
C. R. Qi, H. Su, K. Mo, and L. J. Guibas · 2017
Later among the works it cites.
Label-free supervision of neural networks with physics and domain knowledge
R. Stewart and S. Ermon · 2017
Later among the works it cites.
N. Watters, A. Tacchetti, T. Weber, R. Pascanu, P. Battaglia, and D. Zoran · 2017
Later among the works it cites.
Learning to see physics via visual de-animation
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Newtonian image understanding: Unfolding the dynamics of objects in static images
R. Mottaghi, H. Bagherinezhad, M. Rastegari, and A. Farhadi · 2016
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“what happens if…” learning to predict the effect of forces in images
R. Mottaghi, M. Rastegari, A. Gupta, and A. Farhadi · 2016
Cited alongside, same era.
Physics 101: Learning physical object properties from unlabeled videos
J. Wu, J. J. Lim, H. Zhang, J. B. Tenenbaum, and W. T. Freeman · 2016
Cited alongside, same era.
A comparative evaluation of approximate probabilistic simulation and deep neural networks as accounts of human physical scene understanding
R. Zhang, J. Wu, C. Zhang, W. T. Freeman, and J. B. Tenenbaum · 2016
Cited alongside, same era.
Se3-nets: Learning rigid body motion using deep neural networks
A. Byravan and D. Fox · 2017
Cited alongside, same era.
A compositional object-based approach to learning physical dynamics
M. B. Chang, T. Ullman, A. Torralba, and J. B. Tenenbaum · 2017
Cited alongside, same era.
Learning A Physical Long-term Predictor
S. Ehrhardt, A. Monszpart, N. J. Mitra, and A. Vedaldi · 2017
Cited alongside, same era.
J. Wu, E. Lu, P. Kohli, W. T. Freeman, and J. B. Tenenbaum · 2017
Later among the works it cites.
Unsupervised intuitive physics from visual observations
S. Ehrhardt, A. Monszpart, N. J. Mitra, and A. Vedaldi · 2018
Later among the works it cites.
Physical primitive decomposition
Z. Liu, W. T. Freeman, J. B. Tenenbaum, and J. Wu · 2018
Later among the works it cites.
Flexible neural representation for physics prediction
D. Mrowca, C. Zhuang, E. Wang, N. Haber, L. Fei-Fei, J. B. Tenenbaum, and D. L. K. Yamins · 2018
Later among the works it cites.
Intphys: A framework and benchmark for visual intuitive physics reasoning
R. Riochet, M. Y. Castro, M. Bernard, A. Lerer, R. Fergus, V. Izard, and E. Dupoux · 2018
Later among the works it cites.
Graph networks as learnable physics engines for inference and control
A. Sanchez-Gonzalez, N. Heess, J. T. Springenberg, J. Merel, M. Riedmiller, R. Hadsell, and P. Battaglia · 2018
Later among the works it cites.
3d-physnet: Learning the intuitive physics of non-rigid object deformations
Z. Wang, S. Rosa, B. Yang, S. Wang, N. Trigoni, and A. Markham · 2018
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Interpretable intuitive physics model
T. Ye, X. Wang, J. Davidson, and A. Gupta · 2018
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