  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 conditions and obstructions (e.g., handwear). The benchmark includes a
multi-view and multi-spectral dataset collected from 28 subjects performing
hand-object and hand-virtual interactions under diverse scenarios, accurately
annotated with 3D hand poses through an automated process. We introduce a new
baseline method, TherFormer, utilizing dual transformer modules for effective
egocentric 3D hand pose estimation in thermal imagery. Our experimental results
highlight TherFormer's leading performance and affirm thermal imaging's
effectiveness in enabling robust 3D hand pose estimation in adverse conditions.
