ThermoHands: A Benchmark for 3D Hand Pose Estimation from Egocentric
  Thermal Image
Fangqiang Ding Yunzhou Zhu Xiangyu Wen Chris Xiaoxuan Lu
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.