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This work considers identifying parameters characterizing a physical system's dynamic motion directly from a video whose rendering configurations are inaccessible.
Fluid control using the adjoint method
Antoine McNamara, Adrien Treuille, Zoran Popović, and Jos Stam · 2004
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A first-order analysis of lighting, shading, and shadows
Ravi Ramamoorthi, Dhruv Mahajan, and Peter Belhumeur · 2007
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Theory, analysis and applications of 2d global illumination
Wojciech Jarosz, Volker Schönefeld, Leif Kobbelt, and Henrik Wann Jensen · 2012
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Anisotropic gaussian mutations for metropolis light transport through hessian-hamiltonian dynamics
Tzu-Mao Li, Jaakko Lehtinen, Ravi Ramamoorthi, Wenzel Jakob, and Frédo Durand · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Perceptual losses for real-time style transfer and super-resolution
Justin Johnson, Alexandre Alahi, and Li Fei-Fei · 2016
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SGDR: Stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter · 2016
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Physically Based Rendering: From Theory to Implementation
Matt Pharr, Wenzel Jakob, and Greg Humphreys · 2016
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Instance normalization: The missing ingredient for fast stylization
Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky · 2016
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Searching for activation functions
Prajit Ramachandran, Barret Zoph, and Quoc V Le · 2017
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CAD2RL: Real single-image flight without a single real image
Fereshteh Sadeghi and Sergey Levine · 2017
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Domain randomization for transferring deep neural networks from simulation to the real world
Josh Tobin, Rachel Fong, Alex Ray, Jonas Schneider, Wojciech Zaremba, and Pieter Abbeel · 2017
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Unpaired image-to-image translation using cycle-consistent adversarial networks
Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A. Efros · 2017
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End-to-end differentiable physics for learning and control
Filipe de Avila Belbute-Peres, Kevin Smith, Kelsey Allen, Josh Tenenbaum, and J Zico Kolter · 2018
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Differentiable monte carlo ray tracing through edge sampling
Tzu-Mao Li, Miika Aittala, Frédo Durand, and Jaakko Lehtinen · 2018
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Sim-to-real transfer of robotic control with dynamics randomization
Xue Bin Peng, Marcin Andrychowicz, Wojciech Zaremba, and Pieter Abbeel · 2018
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Sim-to-real: Learning agile locomotion for quadruped robots
Jie Tan, Tingnan Zhang, Erwin Coumans, Atil Iscen, Yunfei Bai, Danijar Hafner, Steven Bohez, and Vincent Vanhoucke · 2018
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A differentiable physics engine for deep learning in robotics
ADD: Analytically differentiable dynamics for multi-body systems with frictional contact
Moritz Geilinger, David Hahn, Jonas Zehnder, Moritz Bächer, Bernhard Thomaszewski, and Stelian Coros · 2020
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PlasticineLab: A soft-body manipulation benchmark with differentiable physics
Zhiao Huang, Yuanming Hu, Tao Du, Siyuan Zhou, Hao Su, Joshua B Tenenbaum, and Chuang Gan · 2020
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NeRF: Representing scenes as neural radiance fields for view synthesis
Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng · 2020
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gradSim: Differentiable simulation for system identification and visuomotor control
J Krishna Murthy, Miles Macklin, Florian Golemo, Vikram Voleti, Linda Petrini, Martin Weiss, Breandan Considine, Jérôme Parent-Lévesque, Kevin Xie, Kenny Erleben, et al · 2020
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Scalable differentiable physics for learning and control
Yi-Ling Qiao, Junbang Liang, Vladlen Koltun, and Ming Lin · 2020
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Jonas Degrave, Michiel Hermans, Joni Dambre, et al · 2019
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Real2Sim: Visco-elastic parameter estimation from dynamic motion
David Hahn, Pol Banzet, James M. Bern, and Stelian Coros · 2019
Cited alongside, same era.
ChainQueen: A real-time differentiable physical simulator for soft robotics
Yuanming Hu, Jiancheng Liu, Andrew Spielberg, Joshua B. Tenenbaum, William T. Freeman, Jiajun Wu, Daniela Rus, and Wojciech Matusik · 2019
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Sim-to-real via sim-to-sim: Data-efficient robotic grasping via randomized-to-canonical adaptation networks
Stephen James, Paul Wohlhart, Mrinal Kalakrishnan, Dmitry Kalashnikov, Alex Irpan, Julian Ibarz, Sergey Levine, Raia Hadsell, and Konstantinos Bousmalis · 2019
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Differentiable cloth simulation for inverse problems
Junbang Liang, Ming Lin, and Vladlen Koltun · 2019
Cited alongside, same era.
Mitsuba 2: A retargetable forward and inverse renderer
Merlin Nimier-David, Delio Vicini, Tizian Zeltner, and Wenzel Jakob · 2019
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Learning dexterous in-hand manipulation
OpenAI: Marcin Andrychowicz, Bowen Baker, Maciek Chociej, Rafal Jozefowicz, Bob McGrew, Jakub Pachocki, Arthur Petron, Matthias Plappert, Glenn Powell, Alex Ray, et al · 2020
Cited alongside, same era.
Underwater soft robot modeling and control with differentiable simulation
Tao Du, Josie Hughes, Sebastien Wah, Wojciech Matusik, and Daniela Rus · 2021
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ThreeDWorld: A platform for interactive multi-modal physical simulation
Chuang Gan, Jeremy Schwartz, Seth Alter, Damian Mrowca, Martin Schrimpf, James Traer, Julian De Freitas, Jonas Kubilius, Abhishek Bhandwaldar, Nick Haber, et al · 2021
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PTR: A benchmark for part-based conceptual, relational, and physical reasoning
Yining Hong, Li Yi, Joshua B Tenenbaum, Antonio Torralba, and Chuang Gan · 2021
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DiffAqua: A differentiable computational design pipeline for soft underwater swimmers with shape interpolation
Pingchuan Ma, Tao Du, John Z. Zhang, Kui Wu, Andrew Spielberg, Robert K. Katzschmann, and Wojciech Matusik · 2021
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OptiTrack motion capture systems
OptiTrack · 2021
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Qualisys motion capture systems
Qualisys · 2021
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VICON: Award-winning motion capture systems
Vicon · 2021
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An end-to-end differentiable framework for contact-aware robot design
Jie Xu, Tao Chen, Lara Zlokapa, Michael Foshey, Wojciech Matusik, Shinjiro Sueda, and Pulkit Agrawal · 2021
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