DUA:一个域统一方法,用于跨数据集的3D人体姿势估计
João Renato Ribeiro Manesco1, Stefano Berretti2, Aparecido Nilceu Marana1
1Faculty of Sciences, UNESP-São Paulo State University, Bauru 17033-360, SP, Brazil.
Sensors (Basel, Switzerland)
|September 9, 2023
概括
本研究引入了一个域统一方法用于3D人体姿势估计,在交叉数据集场景中显著减少了29.24%的错误. 该方法通过整合合成数据来增强概括性,提高现实应用中的准确性.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 机器人技术 机器人技术 机器人技术
背景情况:
- 深度学习在2D人类姿势估计方面表现出色.
- 3D姿势估计提供了更强大的结果,但缺乏足够的标记数据.
- 目前使用二维姿势输入的两步方法在数据集的概括方面遇到了困难.
研究的目的:
- 为了解决跨数据集3D人体姿势估计中的姿势 misalignment.
- 提高3D姿势估计系统的概括能力.
- 为了利用合成数据来增强域名适应.
主要方法:
- 提出了一个新的域统一方法.
- 集成了三个模块:姿势转换器,不确定性估计器和域分类器.
- 采用了使用合成 (SURREAL) 和真实 (Human3.6M) 数据集的域调整技术.
主要成果:
- 与没有适应的场景相比,实现了44.1mm (29.24%) 的误差减少.
- 在交叉数据集评估中展示了最先进的性能.
- 成功地缓解了姿势 misalignment 问题.
结论:
- 统一域的方法有效地提高了3D人体姿势估计的准确性和稳定性.
- 使用合成数据进行域调整对于一般化姿势估计模型至关重要.
- 拟议的方法为现实世界的人类姿势理解提供了重大进展.
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