改进3D人体姿态评估，让测量更准。
Limitations of (Procrustes) Alignment in Assessing Multi-Person Human Pose and Shape Estimation
- 提出RotAvat，优化3D网格与地面的对齐方式
- 提升W-MPJPE和W-PVE等指标的评估准确性
- 适合关注人体姿态估计算法评估的研究者
本文探讨了在视频监控场景中准确估计3D人体姿态与形状的挑战。尽管已有研究提倡使用忽略(Procrustes)重对齐步骤的度量标准如W-MPJPE和W-PVE以改善模型评估，但这些方法仍存在局限性。为此，本文提出RotAvat技术，旨在通过优化3D网格与地面平面的对齐来改进这些度量标准。定性对比实验表明，RotAvat有效克服了现有方法的不足，显著提升了评估的可靠性。
原文摘要 · Abstract (English)
We delve into the challenges of accurately estimating 3D human pose and shape in video surveillance scenarios. Beginning with the advocacy for metrics like W-MPJPE and W-PVE, which omit the (Procrustes) realignment step, to improve model evaluation, we then introduce RotAvat. This technique aims to enhance these metrics by refining the alignment of 3D meshes with the ground plane. Through qualitative comparisons, we demonstrate RotAvat's effectiveness in addressing the limitations of existing aproaches.
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