mlsanityData to 14 Oct 2026

LATTE-MV: Learning to Anticipate Table Tennis Hits from Monocular Videos

arXiv 2503.20936 ↗First posted 26 Mar 2025cs.CV cs.AIcited by 5

Not on the map. Why?

Abstract (arXiv)

Physical agility is a necessary skill in competitive table tennis, but by no means sufficient. Champions excel in this fast-paced and highly dynamic environment by anticipating their opponent's intent - buying themselves the necessary time to react. In this work, we take one step towards designing such an anticipatory agent. Previous works have developed systems capable of real-time table tennis gameplay, though they often do not leverage anticipation. Among the works that forecast opponent actions, their approaches are limited by dataset size and variety. Our paper contributes (1) a scalable system for reconstructing monocular video of table tennis matches in 3D and (2) an uncertainty-aware controller that anticipates opponent actions. We demonstrate in simulation that our policy improves the ball return rate against high-speed hits from 49.9% to 59.0% as compared to a baseline non-anticipatory policy.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 15 of its 48 references that are in the map. First posted on arXiv 26 Mar 2025.

Builds on

15 of 48 references.

References · 15

  1. 2017Attention Is All You Needcited by 60,361
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
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  4. 2020Uncertainty Sets for Image Classifiers using Conformal Predictioncited by 366
  5. 2023Humans in 4D: Reconstructing and Tracking Humans with Transformerscited by 350
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  7. 2024Humanoid Locomotion as Next Token Predictioncited by 69
  8. 2022Uncertainty-Informed Deep Learning Models Enable High-Confidence Predictions for Digital Histopathologycited by 37
  9. 2020TTNet: Real-time temporal and spatial video analysis of table tenniscited by 36
  10. 2020Learning to Play Table Tennis From Scratch using Muscular Robotscited by 35
  11. 2022i-Sim2Real: Reinforcement Learning of Robotic Policies in Tight Human-Robot Interaction Loopscited by 32
  12. 2024Achieving Human Level Competitive Robot Table Tenniscited by 27
3 more
  1. 2020Robotic Table Tennis with Model-Free Reinforcement Learningcited by 15
  2. 2020Sample-efficient Reinforcement Learning in Robotic Table Tenniscited by 12
  3. 2022GoalsEye: Learning High Speed Precision Table Tennis on a Physical Robotcited by 9

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 2.

5 papers.

  1. 2025Uplifting Table Tennis: A Robust, Real-World Application for 3D Trajectory and Spin Estimationcited by 3
  2. 2025RacketVision: A Multiple Racket Sports Benchmark for Unified Ball and Racket Analysiscited by 2
  3. 2025BlurBall: Joint Ball and Motion Blur Estimation for Table Tennis Ball Trackingcited by 1
  4. 2026TT4D: A Pipeline and Dataset for Table Tennis 4D Reconstruction From Monocular Videoscited by 1
  5. 2026How good was my shot? Quantifying Player Skill Level in Table Tenniscited by 0