现有补全动画标记的评估指标与人眼感知不一致，本文提出更符合主观感受的新指标。
Evaluating the Evaluators: Towards Human-aligned Metrics for Missing Markers Reconstruction
- 用主观感知实验发现均方误差与人眼判断无关
- 提出一组与真实观感更相关的评估指标
- 适合关注动画数据质量评估的研究者
动画数据通常通过光学运动捕捉系统获取，该系统使用多个摄像机定位光学标记点。然而，系统误差或遮挡可能导致标记缺失，手动修复耗时费力。这促使学术界关注基于机器学习的缺失标记重建方法。目前大多数论文仅使用均方误差作为主要评估指标。本文揭示该指标与人类对填充质量的主观感知无相关性，并引入并评估了一组更具相关性的新指标，有望推动该领域发展。
原文摘要 · Abstract (English)
Animation data is often obtained through optical motion capture systems, which utilize a multitude of cameras to establish the position of optical markers. However, system errors or occlusions can result in missing markers, the manual cleaning of which can be time-consuming. This has sparked interest in machine learning-based solutions for missing marker reconstruction in the academic community. Most academic papers utilize a simplistic mean square error as the main metric. In this paper, we show that this metric does not correlate with subjective perception of the fill quality. Additionally, we introduce and evaluate a set of better-correlated metrics that can drive progress in the field.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。