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We propose a novel approach for deformation-aware neural networks that learn the weighting and synthesis of dense volumetric deformation fields.
Neuroanimator: Fast neural network emulation and control of physics-based models
Radek Grzeszczuk, Demetri Terzopoulos, and Geoffrey Hinton · 1998
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Computing the physical parameters of rigid-body motion from video
Kiran S Bhat, Steven M Seitz, Jovan Popović, and Pradeep K Khosla · 2002
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Pattern Recognition and Machine Learning (Information Science and Statistics)
Christopher M. Bishop · 2006
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Estimating contact dynamics
Marcus A Brubaker, Leonid Sigal, and David J Fleet · 2009
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Mass and momentum conservation for fluid simulation
Michael Lentine, Mridul Aanjaneya, and Ronald Fedkiw · 2011
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Stacked convolutional auto-encoders for hierarchical feature extraction
Jonathan Masci, Ueli Meier, Dan Cireşan, and Jürgen Schmidhuber · 2011
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Physically plausible 3d scene tracking: The single actor hypothesis
Nikolaos Kyriazis and Antonis Argyros · 2013
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Scalable 3d tracking of multiple interacting objects
Nikolaos Kyriazis and Antonis Argyros · 2014
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Blending Liquids
Karthik Raveendran, Nils Thuerey, Chris Wojtan, and Greg Turk · 2014
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Stochastic backpropagation and approximate inference in deep generative models
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra · 2014
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Fluid Simulation for Computer Graphics
Robert Bridson · 2015
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Flownet: Learning optical flow with convolutional networks
Alexey Dosovitskiy, Philipp Fischery, Eddy Ilg, Caner Hazirbas, Vladimir Golkov, Patrick van der Smagt, Daniel Cremers, Thomas Brox, et al · 2015
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Learning visual predictive models of physics for playing billiards
Katerina Fragkiadaki, Pulkit Agrawal, Sergey Levine, and Jitendra Malik · 2015
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Spatial transformer networks
Max Jaderberg, Karen Simonyan, Andrew Zisserman, et al · 2015
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Data-driven fluid simulations using regression forests
Lubor Ladicky, SoHyeon Jeong, Barbara Solenthaler, Marc Pollefeys, and Markus Gross · 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, Bill Freeman, and Josh Tenenbaum · 2015
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Learning to poke by poking: Experiential learning of intuitive physics
Pulkit Agrawal, Ashvin V Nair, Pieter Abbeel, Jitendra Malik, and Sergey Levine · 2016
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Cnn-based patch matching for optical flow with thresholded hinge loss
Christian Bailer, Kiran Varanasi, and Didier Stricker · 2016
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Interaction networks for learning about objects, relations and physics
Pixel recurrent neural networks
Aaron Van Oord, Nal Kalchbrenner, and Koray Kavukcuoglu · 2016
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Physics 101: Learning physical object properties from unlabeled videos
Jiajun Wu, Joseph J Lim, Hongyi Zhang, Joshua B Tenenbaum, and William T Freeman · 2016
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Data-driven synthesis of smoke flows with CNN-based feature descriptors
Mengyu Chu and Nils Thuerey · 2017
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Learning a physical long-term predictor
Sebastien Ehrhardt, Aron Monszpart, Niloy J Mitra, and Andrea Vedaldi · 2017
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Deep learning the physics of transport phenomena
Amir Barati Farimani, Joseph Gomes, and Vijay S Pande · 2017
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Peter Battaglia, Razvan Pascanu, Matthew Lai, Danilo Jimenez Rezende, et al · 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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Learning to perform physics experiments via deep reinforcement learning
Misha Denil, Pulkit Agrawal, Tejas D Kulkarni, Tom Erez, Peter Battaglia, and Nando de Freitas · 2016
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Unsupervised learning for physical interaction through video prediction
Chelsea Finn, Ian Goodfellow, and Sergey Levine · 2016
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Flownet 2.0: Evolution of optical flow estimation with deep networks
Eddy Ilg, Nikolaus Mayer, Tonmoy Saikia, Margret Keuper, Alexey Dosovitskiy, and Thomas Brox · 2016
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Learning physical intuition of block towers by example
Adam Lerer, Sam Gross, and Rob Fergus · 2016
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To fall or not to fall: A visual approach to physical stability prediction
Wenbin Li, Seyedmajid Azimi, Ale s · 2016
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Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A Efros · 2017
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Pde-net: Learning pdes from data
Zichao Long, Yiping Lu, Xianzhong Ma, and Bin Dong · 2017
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Unflow: Unsupervised learning of optical flow with a bidirectional census loss
Simon Meister, Junhwa Hur, and Stefan Roth · 2017
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Reasoning about liquids via closed-loop simulation
Connor Schenck and Dieter Fox · 2017
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Label-free supervision of neural networks with physics and domain knowledge
Russell Stewart and Stefano Ermon · 2017
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Interpolations of Smoke and Liquid Simulations
Nils Thuerey · 2017
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Accelerating eulerian fluid simulation with convolutional networks
Jonathan Tompson, Kristofer Schlachter, Pablo Sprechmann, and Ken Perlin · 2017
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Visual interaction networks: Learning a physics simulator from video
Nicholas Watters, Daniel Zoran, Theophane Weber, Peter Battaglia, Razvan Pascanu, and Andrea Tacchetti · 2017
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