In this work, we present ThermoHands, a new benchmark for thermal image-based egocentric 3D hand pose estimation, aimed at overcoming challenges like varying lighting and obstructions (e.g., handwear). The benchmark includes a diverse dataset from 28 subjects performing hand-object and hand-virtual interactions, accurately annotated with 3D hand poses through an automated process. We introduce a bespoken baseline method, TheFormer, utilizing dual transformer modules for effective egocentric 3D hand pose estimation in thermal imagery. Our experimental results highlight TheFormer's leading performance and affirm thermal imaging's effectiveness in enabling robust 3D hand pose estimation in adverse conditions.

本文介绍了ThermoHands，一个新的基准测试，用于克服诸如光照变化和遮挡（例如，手套）等挑战，以进行基于热成像的视角内三维手部姿势估计。该基准测试包括来自28名受试者的多样化数据集，他们进行手物体和手虚拟交互，并通过自动化过程准确注释了三维手部姿势。我们引入了一种特制的基线方法TheFormer，利用双变换器模块在热成像中有效地进行视角内三维手部姿势估计。我们的实验结果突出了TheFormer的领先性能，并确认了热成像在恶劣条件下实现强大的三维手部姿势估计的有效性。

ThermoHands: 从主体观察的热图像中进行三维手势估计的基准测试