用舞蹈视频挑战姿态估计，提出新方法并公开数据集。
When Dance Video Archives Challenge Computer Vision
- 融合前沿技术构建3D姿态估计新流程。
- 在舞蹈视频档案上测试，发现数据特性影响显著。
- 结果开源，适合动作分析与视觉研究者参考。
人体姿态估计的准确性和效率依赖于数据质量及其特性。为展示舞蹈视频对姿态估计技术的挑战，我们提出一种结合最新技术的3D人体姿态估计新流程，这些技术此前未用于舞蹈分析。其次，我们在舞蹈视频档案上进行测试与广泛实验，并使用可视化分析工具评估多个数据参数对姿态估计的影响。相关结果已公开，供研究使用：https://www.couleur.org/articles/arXiv-1-2025/
原文摘要 · Abstract (English)
The accuracy and efficiency of human body pose estimation depend on the quality of the data to be processed and of the particularities of these data. To demonstrate how dance videos can challenge pose estimation techniques, we proposed a new 3D human body pose estimation pipeline which combined up-to-date techniques and methods that had not been yet used in dance analysis. Second, we performed tests and extensive experimentations from dance video archives, and used visual analytic tools to evaluate the impact of several data parameters on human body pose. Our results are publicly available for research at https://www.couleur.org/articles/arXiv-1-2025/
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