Fetching the paper…
Reading the bibliography…
Generating diverse and realistic human motion that can physically interact with an environment remains a challenging research area in character animation.
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 · 1911
Earlier work this paper cites.
Denoising Diffusion Probabilistic Models
Jonathan Ho, Ajay Jain, and Pieter Abbeel. 2020 · 2006
Earlier work this paper cites.
Improved Techniques for Training Score-Based Generative Models
Yang Song and Stefano Ermon. 2020 · 2006
Earlier work this paper cites.
A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning. In Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics (Proceedings of Machine Learning Research, Vol. 15) , Geoffrey Gordon, David Dunson, and Miroslav Dudík (Eds.). PMLR, Fort Lauderdale, FL, USA, 627–635
Stephane Ross, Geoffrey Gordon, and Drew Bagnell. 2011 · 2011
Earlier work this paper cites.
UniCon: Universal Neural Controller For Physics-based Character Motion
Tingwu Wang, Yunrong Guo, Maria Shugrina, and Sanja Fidler. 2020 · 2011
Earlier work this paper cites.
Bayesian learning via stochastic gradient Langevin dynamics. In Proceedings of the 28th international conference on machine learning (ICML-11) . 681–688
Max Welling and Yee W Teh. 2011 · 2011
Earlier work this paper cites.
Measurement of foot placement and its variability with inertial sensors
John R Rebula, Lauro V Ojeda, Peter G Adamczyk, and Arthur D Kuo. 2013 · 2013
Earlier work this paper cites.
The KIT motion-language dataset
Matthias Plappert, Christian Mandery, and Tamim Asfour. 2016 · 2016
Earlier work this paper cites.
FiLM: Visual Reasoning with a General Conditioning Layer
Ethan Perez, Florian Strub, Harm de Vries, Vincent Dumoulin, and Aaron C. Courville. 2017 · 2017
Earlier work this paper cites.
Walking with wider steps changes foot placement control, increases kinematic variability and does not improve linear stability
Jennifer A Perry and Manoj Srinivasan. 2017 · 2017
Earlier work this paper 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. 2018 · 2018
Earlier work this paper cites.
DeepMimic: Example-guided Deep Reinforcement Learning of Physics-based Character Skills
Xue Bin Peng, Pieter Abbeel, Sergey Levine, and Michiel van de Panne. 2018 · 2018
Earlier work this paper cites.
DReCon: data-driven responsive control of physics-based characters
Kevin Bergamin, Simon Clavet, Daniel Holden, and James Richard Forbes. 2019 · 2019
Earlier work this paper cites.
AMASS: Archive of Motion Capture as Surface Shapes. In International Conference on Computer Vision . 5442–5451
Naureen Mahmood, Nima Ghorbani, Nikolaus F. Troje, Gerard Pons-Moll, and Michael J. Black. 2019 · 2019
Earlier work this paper cites.
Learning predict-and-simulate policies from unorganized human motion data
Soohwan Park, Hoseok Ryu, Seyoung Lee, Sunmin Lee, and Jehee Lee. 2019 · 2019
Earlier work this paper cites.
Bump’em: an Open-Source, Bump-Emulation System for Studying Human Balance and Gait. In 2020 IEEE International Conference on Robotics and Automation (ICRA) . 9093–9099
Guan Rong Tan, Michael Raitor, and Steven H. Collins. 2020 · 2020
Earlier work this paper cites.
A scalable approach to control diverse behaviors for physically simulated characters
Jungdam Won, Deepak Gopinath, and Jessica Hodgins. 2020a · 2020
Cited alongside, same era.
A scalable approach to control diverse behaviors for physically simulated characters
Jungdam Won, Deepak Gopinath, and Jessica Hodgins. 2020b · 2020
Cited alongside, same era.
Learning locomotion skills for cassie: Iterative design and sim-to-real. In Conference on Robot Learning . PMLR, 317–329
Zhaoming Xie, Patrick Clary, Jeremy Dao, Pedro Morais, Jonanthan Hurst, and Michiel Panne. 2020 · 2020
Cited alongside, same era.
SuperTrack: motion tracking for physically simulated characters using supervised learning
Levi Fussell, Kevin Bergamin, and Daniel Holden. 2021 · 2021
Cited alongside, same era.
Reinforcement learning for robust parameterized locomotion control of bipedal robots. In 2021 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2811–2817
Zhongyu Li, Xuxin Cheng, Xue Bin Peng, Pieter Abbeel, Sergey Levine, Glen Berseth, and Koushil Sreenath. 2021 · 2021
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
Albert Gu and Tri Dao. 2023 · 2023
Later among the works it cites.
Shared Autonomy Locomotion Synthesis with a Virtual Powered Prosthetic Ankle
Balint K Hodossy and Dario Farina. 2023 · 2023
Later among the works it cites.
Physical Simulation of Balance Recovery after a Push. In Proceedings of the 16th ACM SIGGRAPH Conference on Motion, Interaction and Games (<conf-loc>, <city>Rennes</city>, <country>France</country>, </conf-loc>) (MIG ’23) . Association for Computing Machinery, New York, NY, USA, Article 23, 11 pages
Alexis Jensen, Thomas Chatagnon, Niloofar Khoshsiyar, Daniele Reda, Michiel Van De Panne, Charles Pontonnier, and Julien Pettré. 2023 · 2023
Later among the works it cites.
Development of a new robust stable walking algorithm for a humanoid robot using deep reinforcement learning with multi-sensor data fusion
Çağrı Kaymak, Ayşegül Uçar, and Cüneyt Güzeliş. 2023 · 2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
AMP: Adversarial Motion Priors for Stylized Physics-Based Character Control
Xue Bin Peng, Ze Ma, Pieter Abbeel, Sergey Levine, and Angjoo Kanazawa. 2021 · 2021
Cited alongside, same era.
Learning Transferable Visual Models From Natural Language Supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. 2021 · 2021
Cited alongside, same era.
Generating Diverse and Natural 3D Human Motions From Text. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . 5152–5161
Chuan Guo, Shihao Zou, Xinxin Zuo, Sen Wang, Wei Ji, Xingyu Li, and Li Cheng. 2022 · 2022
Cited alongside, same era.
Ase: Large-scale reusable adversarial skill embeddings for physically simulated characters
Xue Bin Peng, Yunrong Guo, Lina Halper, Sergey Levine, and Sanja Fidler. 2022 · 2022
Cited alongside, same era.
EDGE: Editable Dance Generation From Music
Jonathan Tseng, Rodrigo Castellon, and C. Karen Liu. 2022 · 2022
Cited alongside, same era.
Physics-based character controllers using conditional VAEs
Jungdam Won, Deepak Gopinath, and Jessica Hodgins. 2022 · 2022
Cited alongside, same era.
ControlVAE: Model-Based Learning of Generative Controllers for Physics-Based Characters
Heyuan Yao, Zhenhua Song, Baoquan Chen, and Libin Liu. 2022 · 2022
Cited alongside, same era.
Perpetual Humanoid Control for Real-time Simulated Avatars
Zhengyi Luo, Jinkun Cao, Alexander Winkler, Kris Kitani, and Weipeng Xu. 2023 · 2023
Later among the works it cites.
Diffusion Co-Policy for Synergistic Human-Robot Collaborative Tasks
Eley Ng, Ziang Liu, and Monroe Kennedy. 2024 · 2023
Later among the works it cites.
Learning bipedal walking for humanoids with current feedback
Rohan P Singh, Zhaoming Xie, Pierre Gergondet, and Fumio Kanehiro. 2023 · 2023
Later among the works it cites.
Human Motion Diffusion Model. In The Eleventh International Conference on Learning Representations
Guy Tevet, Sigal Raab, Brian Gordon, Yoni Shafir, Daniel Cohen-or, and Amit Haim Bermano. 2023 · 2023
Later among the works it cites.
Edge: Editable dance generation from music. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 448–458
Jonathan Tseng, Rodrigo Castellon, and Karen Liu. 2023 · 2023
Later among the works it cites.
AddBiomechanics: Automating model scaling, inverse kinematics, and inverse dynamics from human motion data through sequential optimization
Keenon Werling, Nicholas A Bianco, Michael Raitor, Jon Stingel, Jennifer L Hicks, Steven H Collins, Scott L Delp, and C Karen Liu. 2023 · 2023
Later among the works it cites.
MoConVQ: Unified Physics-Based Motion Control via Scalable Discrete Representations
Heyuan Yao, Zhenhua Song, Yuyang Zhou, Tenglong Ao, Baoquan Chen, and Libin Liu. 2023 · 2023
Later among the works it cites.
One-step Diffusion with Distribution Matching Distillation
Tianwei Yin, Michaël Gharbi, Richard Zhang, Eli Shechtman, Fredo Durand, William T. Freeman, and Taesung Park. 2023 · 2023
Later among the works it cites.
Neural Categorical Priors for Physics-Based Character Control
Qingxu Zhu, He Zhang, Mengting Lan, and Lei Han. 2023 · 2023
Later among the works it cites.
DiffuseLoco: Real-Time Legged Locomotion Control with Diffusion from Offline Datasets
Xiaoyu Huang, Yufeng Chi, Ruofeng Wang, Zhongyu Li, Xue Bin Peng, Sophia Shao, Borivoje Nikolic, and Koushil Sreenath. 2024 · 2024
Closest in time.
Universal Humanoid Motion Representations for Physics-Based Control
Zhengyi Luo, Jinkun Cao, Josh Merel, Alexander Winkler, Jing Huang, Kris Kitani, and Weipeng Xu. 2024 · 2024
Closest in time.