对比多个手部姿态数据集，发现真实与合成数据各有优势。
Benchmarking 2D Egocentric Hand Pose Datasets
- 提出新评估协议，综合分析数据质量与模型表现
- 多数数据集针对特定场景，缺乏通用基准
- 真实数据首选H2O，合成数据首选GANerated Hands
从第一人称视频中进行手部姿态估计在人机交互、辅助技术、活动识别和机器人等领域具有广泛应用，是研究热点。现代机器学习模型的性能高度依赖训练数据的质量。本文系统分析了当前用于2D手部姿态估计的第一人称数据集，提出一种新型评估协议，不仅涵盖数据集特征分析与质量评估，还通过状态领先的手部姿态估计模型检测数据缺陷。研究发现，尽管已有众多第一人称数据集可用于2D手部姿态估计，但多数仅针对特定应用场景设计。目前尚无理想的基准数据集；然而，H2O作为真实数据集、GANerated Hands作为合成数据集展现出最佳潜力。
原文摘要 · Abstract (English)
Hand pose estimation from egocentric video has broad implications across various domains, including human-computer interaction, assistive technologies, activity recognition, and robotics, making it a topic of significant research interest. The efficacy of modern machine learning models depends on the quality of data used for their training. Thus, this work is devoted to the analysis of state-of-the-art egocentric datasets suitable for 2D hand pose estimation. We propose a novel protocol for dataset evaluation, which encompasses not only the analysis of stated dataset characteristics and assessment of data quality, but also the identification of dataset shortcomings through the evaluation of state-of-the-art hand pose estimation models. Our study reveals that despite the availability of numerous egocentric databases intended for 2D hand pose estimation, the majority are tailored for specific use cases. There is no ideal benchmark dataset yet; however, H2O and GANerated Hands datasets emerge as the most promising real and synthetic datasets, respectively.
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