# Research team builds egocentric tennis dataset and evaluates vision models' spatiotemporal prediction capabilities

- Source: arXiv cs.RO
- Published: 2026-10-28T04:00:00.000Z
- RoboHot: https://robohot.news/en/items/sb85b8k0himdbjtq3b28jr6zz
- Original: https://arxiv.org/abs/2610.37619

## Summary

The research team collected an annotated egocentric tennis dataset and trained and evaluated image encoders, video encoders, and vision-language models on seven spatiotemporal prediction tasks. The best models achieved current-frame localization errors of 0.14 m for the egocentric player and 0.54 m for the ball, with trajectory prediction errors of 0.34 m and 0.91 m respectively within 1.1 seconds, and a mean error of 0.14 seconds for contact-time prediction. Auxiliary depth information notably improved 3D ball localization, while longer image histories did not improve ball trajectory prediction; the team also proposed a conditional diffusion model that generates full-body tennis kinematics from egocentric video at real-time rates.