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CalTennis dataset released for 3D human pose estimation research

By PulseAugur Editorial · [2 sources] ·

Researchers have introduced CalTennis, a large-scale video dataset designed for evaluating monocular-to-3D human pose estimation. The dataset features over 11 million frames of tennis play from 40 players, captured using multiple synchronized cameras. This benchmark is significantly larger than existing datasets and enables label-free evaluation of pose estimation algorithms, highlighting current model limitations in depth and foot contact estimation while proposing new metrics for performance analysis. AI

IMPACT Provides a large-scale benchmark for advancing monocular-to-3D pose estimation, potentially improving applications in sports analytics and human-computer interaction.

RANK_REASON The cluster describes a new dataset and benchmark published on arXiv for computer vision research.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

CalTennis dataset released for 3D human pose estimation research

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COVERAGE [2]

  1. arXiv cs.CV TIER_1 English(EN) · Ilona Demler, Xinran Xie, Blake Werner, Anna Szczuka, Pietro Perona ·

    CalTennis: Large Multi-View Tennis Video Dataset and Benchmark of Monocular-to-3D Pose Estimation

    arXiv:2606.20542v1 Announce Type: new Abstract: The Caltech Tennis Dataset (CalTennis) is a large-scale video benchmark for evaluating monocular-to-3D pose estimation in the wild. CalTennis comprises over 11 million frames (51 hours) of tennis practice and match play from 40 play…

  2. arXiv cs.CV TIER_1 English(EN) · Pietro Perona ·

    CalTennis: Large Multi-View Tennis Video Dataset and Benchmark of Monocular-to-3D Pose Estimation

    The Caltech Tennis Dataset (CalTennis) is a large-scale video benchmark for evaluating monocular-to-3D pose estimation in the wild. CalTennis comprises over 11 million frames (51 hours) of tennis practice and match play from 40 players, captured with 2-6 synchronized cameras at 6…

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