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This study investigates the robustness and precision of 2D human pose estimation techniques, particularly feature-based and 3D shape-based methods, against the backdrop of varying color illumination conditions simulated by chromatic adaptation, as well as varying spatio-temporal dynamics. While the AIST++ dance videos dataset serves as the primary data, the insights gained are pertinent to broader contexts like sports, highlighting the critical influence of lighting on the performance of pose estimation technologies.

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ChromaPose: Robustness of 2D Pose Estimation Under Different Color Illuminations

  • 会议论文

  • 发表日期：

    2025年

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  • Jamiu Oluwaseun Ojeleye,
  • Pratik Singh Bisht,
  • Philippe Colantoni,
  • Damien Muselet,
  • Alain Tremeau

摘要

This study investigates the robustness and precision of 2D human pose estimation techniques, particularly feature-based and 3D shape-based methods, against the backdrop of varying color illumination conditions simulated by chromatic adaptation, as well as varying spatio-temporal dynamics. While the AIST++ dance videos dataset serves as the primary data, the insights gained are pertinent to broader contexts like sports, highlighting the critical influence of lighting on the performance of pose estimation technologies.