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The field of physics-based animation is gaining importance due to the increasing demand for realism in video games and films, and has recently seen wide adoption of data-driven techniques, such as deep reinforcement learning (RL), which learn control from (human) demonstrations.
Policy gradient methods for reinforcement learning with function approximation. In Advances in neural information processing systems . 1057–1063
Richard S Sutton, David A McAllester, Satinder P Singh, and Yishay Mansour. 2000 · 2000
Earlier work this paper cites.
Interactive motion generation from examples
Okan Arikan and David A Forsyth. 2002 · 2002
Earlier work this paper cites.
Motion graphs
L Kovar. 2002 · 2002
Earlier work this paper cites.
Interactive control of avatars animated with human motion data. In Proceedings of the 29th annual conference on Computer graphics and interactive techniques . 491–500
Jehee Lee, Jinxiang Chai, Paul SA Reitsma, Jessica K Hodgins, and Nancy S Pollard. 2002 · 2002
Earlier work this paper cites.
Style-based inverse kinematics
Keith Grochow, Steven L Martin, Aaron Hertzmann, and Zoran Popović. 2004 · 2004
Earlier work this paper cites.
Automated extraction and parameterization of motions in large data sets
Lucas Kovar and Michael Gleicher. 2004 · 2004
Earlier work this paper cites.
Robust constrained model predictive control
Arthur George Richards. 2005 · 2005
Earlier work this paper cites.
Construction and optimal search of interpolated motion graphs
Alla Safonova and Jessica K Hodgins. 2007 · 2007
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition . Ieee, 248–255
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. 2009 · 2009
Earlier work this paper cites.
A survey of numerical methods for optimal control
Anil V Rao. 2009 · 2009
Earlier work this paper cites.
Motion Fields for Interactive Character Locomotion. In ACM SIGGRAPH Asia 2010 Papers (Seoul, South Korea) (SIGGRAPH ASIA ’10) . Association for Computing Machinery, New York, NY, USA, Article 138, 8 pages
Yongjoon Lee, Kevin Wampler, Gilbert Bernstein, Jovan Popović, and Zoran Popović. 2010 · 2010
Earlier work this paper cites.
Sampling-based contact-rich motion control
Libin Liu, KangKang Yin, Michiel van de Panne, Tianjia Shao, and Weiwei Xu. 2010 · 2010
Earlier work this paper cites.
Terrain-adaptive bipedal locomotion control
Jia-chi Wu and Zoran Popović. 2010 · 2010
Earlier work this paper cites.
Empirical evaluation methods for multiobjective reinforcement learning algorithms
Peter Vamplew, Richard Dazeley, Adam Berry, Rustam Issabekov, and Evan Dekker. 2011 · 2011
Earlier work this paper cites.
Interactive character animation using simulated physics: A state-of-the-art review. In Computer graphics forum , Vol. 31. Wiley Online Library, 2492–2515
Thomas Geijtenbeek and Nicolas Pronost. 2012 · 2012
Earlier work this paper cites.
Continuous character control with low-dimensional embeddings
Sergey Levine, Jack M Wang, Alexis Haraux, Zoran Popović, and Vladlen Koltun. 2012 · 2012
Earlier work this paper cites.
Motion graphs++ a compact generative model for semantic motion analysis and synthesis
Jianyuan Min and Jinxiang Chai. 2012 · 2012
Earlier work this paper cites.
Synthesis and stabilization of complex behaviors through online trajectory optimization. In Intelligent Robots and Systems (IROS), 2012 IEEE/RSJ International Conference on . IEEE, 4906–4913
Yuval Tassa, Tom Erez, and Emanuel Todorov. 2012 · 2012
Earlier work this paper cites.
Generative adversarial nets. In Advances in neural information processing systems . 2672–2680
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014 · 2014
Earlier work this paper cites.
Multiobjective reinforcement learning: A comprehensive overview
Chunming Liu, Xin Xu, and Dewen Hu. 2014 · 2014
Earlier work this paper cites.
Motion Matching-The Road to Next-Gen Animation. In Nucl. ai Conference
Michael Buttner. 2015 · 2015
Earlier work this paper cites.
Online control of simulated humanoids using particle belief propagation
Perttu Hämäläinen, Joose Rajamäki, and C Karen Liu. 2015 · 2015
Earlier work this paper cites.
CMU graphics lab motion capture database
Jessica Hodgins. 2015 · 2015
Earlier work this paper cites.
Improving Sampling-based Motion Control. In Computer Graphics Forum , Vol. 34. Wiley Online Library, 415–423
Libin Liu, KangKang Yin, and Baining Guo. 2015 · 2015
Earlier work this paper cites.
Task-based locomotion
Shailen Agrawal and Michiel van de Panne. 2016 · 2016
Cited alongside, same era.
Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba. 2016 · 2016
Cited alongside, same era.
Motion matching and the road to next-gen animation
Simon Clavet. 2016 · 2016
Cited alongside, same era.
Learning and transfer of modulated locomotor controllers
Nicolas Heess, Greg Wayne, Yuval Tassa, Timothy Lillicrap, Martin Riedmiller, and David Silver. 2016 · 2016
Cited alongside, same era.
Generative adversarial imitation learning. In Advances in Neural Information Processing Systems . 4565–4573
Jonathan Ho and Stefano Ermon. 2016 · 2016
Cited alongside, same era.
Hierarchical visuomotor control of humanoids
Josh Merel, Arun Ahuja, Vu Pham, Saran Tunyasuvunakool, Siqi Liu, Dhruva Tirumala, Nicolas Heess, and Greg Wayne. 2018a · 2018
Later among the works it cites.
Neural probabilistic motor primitives for humanoid control
Josh Merel, Leonard Hasenclever, Alexandre Galashov, Arun Ahuja, Vu Pham, Greg Wayne, Yee Whye Teh, and Nicolas Heess. 2018b · 2018
Later among the works it cites.
Deepmimic: Example-guided deep reinforcement learning of physics-based character skills
Xue Bin Peng, Pieter Abbeel, Sergey Levine, and Michiel van de Panne. 2018a · 2018
Later among the works it cites.
Sfv: Reinforcement learning of physical skills from videos
Xue Bin Peng, Angjoo Kanazawa, Jitendra Malik, Pieter Abbeel, and Sergey Levine. 2018b · 2018
Later among the works it cites.
Mode-adaptive neural networks for quadruped motion control
He Zhang, Sebastian Starke, Taku Komura, and Jun Saito. 2018 · 2018
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Guided learning of control graphs for physics-based characters
Libin Liu, Michiel Van De Panne, and KangKang Yin. 2016 · 2016
Cited alongside, same era.
Visualizing and understanding atari agents
Sam Greydanus, Anurag Koul, Jonathan Dodge, and Alan Fern. 2017 · 2017
Cited alongside, same era.
Phase-functioned neural networks for character control
Daniel Holden, Taku Komura, and Jun Saito. 2017 · 2017
Cited alongside, same era.
Imbalanced-learn: A python toolbox to tackle the curse of imbalanced datasets in machine learning
Guillaume Lemaître, Fernando Nogueira, and Christos K Aridas. 2017 · 2017
Cited alongside, same era.
Learning to schedule control fragments for physics-based characters using deep q-learning
Libin Liu and Jessica Hodgins. 2017 · 2017
Cited alongside, same era.
Learning human behaviors from motion capture by adversarial imitation
Josh Merel, Yuval Tassa, Sriram Srinivasan, Jay Lemmon, Ziyu Wang, Greg Wayne, and Nicolas Heess. 2017 · 2017
Cited alongside, same era.
Deeploco: Dynamic locomotion skills using hierarchical deep reinforcement learning
Xue Bin Peng, Glen Berseth, KangKang Yin, and Michiel Van De Panne. 2017 · 2017
Cited alongside, same era.
Later among the works it cites.
Auto-conditioned recurrent networks for extended complex human motion synthesis
Yi Zhou, Zimo Li, Shuangjiu Xiao, Chong He, Zeng Huang, and Hao Li. 2018 · 2018
Later among the works it cites.
DReCon: data-driven responsive control of physics-based characters
Kevin Bergamin, Simon Clavet, Daniel Holden, and James Richard Forbes. 2019 · 2019
Later among the works it cites.
Machine Learning for Motion Synthesis and Character Control. In Interactive 3D Graphics and Games (I3D) 2019
Michael Buttner. 2019 · 2019
Later among the works it cites.
Model Predictive Control with a Visuomotor System for Physics-based Character Animation
Haegwang Eom, Daseong Han, Joseph S Shin, and Junyong Noh. 2019 · 2019
Later among the works it cites.
Non-smooth Newton Methods for Deformable Multi-body Dynamics
Miles Macklin, Kenny Erleben, Matthias Müller, Nuttapong Chentanez, Stefan Jeschke, and Viktor Makoviychuk. 2019 · 2019
Later among the works it cites.
Learning predict-and-simulate policies from unorganized human motion data
Soohwan Park, Hoseok Ryu, Seyoung Lee, Sunmin Lee, and Jehee Lee. 2019 · 2019
Later among the works it cites.
MCP: Learning Composable Hierarchical Control with Multiplicative Compositional Policies
Xue Bin Peng, Michael Chang, Grace Zhang, Pieter Abbeel, and Sergey Levine. 2019 · 2019
Later among the works it cites.
Neural state machine for character-scene interactions
Sebastian Starke, He Zhang, Taku Komura, and Jun Saito. 2019 · 2019
Later among the works it cites.
Benchmarking model-based reinforcement learning
Tingwu Wang, Xuchan Bao, Ignasi Clavera, Jerrick Hoang, Yeming Wen, Eric Langlois, Shunshi Zhang, Guodong Zhang, Pieter Abbeel, and Jimmy Ba. 2019 · 2019
Later among the works it cites.
Skeleton-Aware Networks for Deep Motion Retargeting
Kfir Aberman, Peizhuo Li, Dani Lischinski, Olga Sorkine-Hornung, Daniel Cohen-Or, and Baoquan Chen. 2020 · 2020
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X3D: Expanding Architectures for Efficient Video Recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 203–213
Christoph Feichtenhofer. 2020 · 2020
Closest in time.
Weakly-Supervised 3D Human Pose Learning via Multi-view Images in the Wild
Umar Iqbal, Pavlo Molchanov, and Jan Kautz. 2020 · 2020
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Fast and flexible multilegged locomotion using learned centroidal dynamics
Taesoo Kwon, Yoonsang Lee, and Michiel Van De Panne. 2020 · 2020
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Character controllers using motion VAEs
Hung Yu Ling, Fabio Zinno, George Cheng, and Michiel Van De Panne. 2020 · 2020
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CARL: Controllable Agent with Reinforcement Learning for Quadruped Locomotion
Ying-Sheng Luo, Jonathan Hans Soeseno, Trista Pei-Chun Chen, and Wei-Chao Chen. 2020 · 2020
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Catch & Carry: reusable neural controllers for vision-guided whole-body tasks
Josh Merel, Saran Tunyasuvunakool, Arun Ahuja, Yuval Tassa, Leonard Hasenclever, Vu Pham, Tom Erez, Greg Wayne, and Nicolas Heess. 2020 · 2020
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Finegym: A hierarchical video dataset for fine-grained action understanding. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 2616–2625
Dian Shao, Yue Zhao, Bo Dai, and Dahua Lin. 2020 · 2020
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A scalable approach to control diverse behaviors for physically simulated characters
Jungdam Won, Deepak Gopinath, and Jessica Hodgins. 2020 · 2020
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