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Learning Physically Simulated Tennis Skills from Broadcast Videos

Authors:

Viktor Makoviychuk,

Kayvon FatahalianAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 42, Issue 4

Article No.: 95, Pages 1 - 14

https://doi.org/10.1145/3592408

Published: 26 July 2023 Publication History

Abstract

We present a system that learns diverse, physically simulated tennis skills from large-scale demonstrations of tennis play harvested from broadcast videos. Our approach is built upon hierarchical models, combining a low-level imitation policy and a high-level motion planning policy to steer the character in a motion embedding learned from broadcast videos. When deployed at scale on large video collections that encompass a vast set of examples of real-world tennis play, our approach can learn complex tennis shotmaking skills and realistically chain together multiple shots into extended rallies, using only simple rewards and without explicit annotations of stroke types. To address the low quality of motions extracted from broadcast videos, we correct estimated motion with physics-based imitation, and use a hybrid control policy that overrides erroneous aspects of the learned motion embedding with corrections predicted by the high-level policy. We demonstrate that our system produces controllers for physically-simulated tennis players that can hit the incoming ball to target positions accurately using a diverse array of strokes (serves, forehands, and backhands), spins (topspins and slices), and playing styles (one/two-handed backhands, left/right-handed play). Overall, our system can synthesize two physically simulated characters playing extended tennis rallies with simulated racket and ball dynamics. Code and data for this work is available at https://research.nvidia.com/labs/toronto-ai/vid2player3d/.

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Cited By

Gao FShi LZhang SWang ZWu YSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)Adaptive-gradient policy optimizationProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3692664(14844-14858)Online publication date: 21-Jul-2024
https://dl.acm.org/doi/10.5555/3692070.3692664
He YYuan ZWu YCheng LDeng DWu YWooldridge MDy JNatarajan S(2024)ViSTecProceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence and Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence and Fourteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v38i8.28692(8490-8498)Online publication date: 20-Feb-2024
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Tao DRuizhen HLibin LLi YHao Z(2024)Research progress in human-like indoor scene interactionJournal of Image and Graphics10.11834/jig.24000429:6(1575-1606)Online publication date: 2024
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Index Terms

Learning Physically Simulated Tennis Skills from Broadcast Videos
1. Computing methodologies
  1. Computer graphics
    1. Animation

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cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 42, Issue 4

August 2023

1912 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/3609020

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Publication History

Published: 26 July 2023

Published in TOG Volume 42, Issue 4

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Cited By

Gao FShi LZhang SWang ZWu YSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)Adaptive-gradient policy optimizationProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3692664(14844-14858)Online publication date: 21-Jul-2024
https://dl.acm.org/doi/10.5555/3692070.3692664
He YYuan ZWu YCheng LDeng DWu YWooldridge MDy JNatarajan S(2024)ViSTecProceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence and Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence and Fourteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v38i8.28692(8490-8498)Online publication date: 20-Feb-2024
https://dl.acm.org/doi/10.1609/aaai.v38i8.28692
Tao DRuizhen HLibin LLi YHao Z(2024)Research progress in human-like indoor scene interactionJournal of Image and Graphics10.11834/jig.24000429:6(1575-1606)Online publication date: 2024
https://doi.org/10.11834/jig.240004
Wu YDou ZIshiwaka YOgawa SLou YWang WLiu LKomura T(2024)CBIL: Collective Behavior Imitation Learning for Fish from Real VideosACM Transactions on Graphics10.1145/368790443:6(1-17)Online publication date: 19-Dec-2024
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Yao HSong ZZhou YAo TChen BLiu L(2024)MoConVQ: Unified Physics-Based Motion Control via Scalable Discrete RepresentationsACM Transactions on Graphics10.1145/365813743:4(1-21)Online publication date: 19-Jul-2024
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Hu JHui KLiu ZZhang HFu C(2024)CNS-Edit: 3D Shape Editing via Coupled Neural Shape OptimizationACM SIGGRAPH 2024 Conference Papers10.1145/3641519.3657412(1-12)Online publication date: 13-Jul-2024
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Abdallah AKasem MAbdalla MMahmoud MElkasaby MElbendary YJatowt AHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)ArabicaQA: A Comprehensive Dataset for Arabic Question AnsweringProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657889(2049-2059)Online publication date: 10-Jul-2024
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