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

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.

Part of the event: Egocentric Tennis Dataset Evaluates Vision Models for Spatiotemporal Prediction

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