ThermoHands: A Benchmark for 3D Hand Pose Estimation from Egocentric Thermal Images
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Abstract (arXiv)
Designing egocentric 3D hand pose estimation systems that can perform reliably in complex, real-world scenarios is crucial for downstream applications. Previous approaches using RGB or NIR imagery struggle in challenging conditions: RGB methods are susceptible to lighting variations and obstructions like handwear, while NIR techniques can be disrupted by sunlight or interference from other NIR-equipped devices. To address these limitations, we present ThermoHands, the first benchmark focused on thermal image-based egocentric 3D hand pose estimation, demonstrating the potential of thermal imaging to achieve robust performance under these conditions. 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.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 59 of its 119 references that are in the map. First posted on arXiv 14 Mar 2024.
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59 of 119 references.
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Citing papers per year: 2024: 1; 2025: 3.
4 papers.
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- 2025THOR: Thermal-guided Hand-Object Reasoning via Adaptive Vision Samplingcited by 0
- 2025M4Human: A Large-Scale Multimodal mmWave Radar Benchmark for Human Mesh Reconstruction13 Dec 2025
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- 2025Bridging Perspectives: A Survey on Cross-view Collaborative Intelligence with Egocentric-Exocentric Vision6 Jun 2025
- 2024Egocentric and Exocentric Methods: A Short Survey27 Oct 2024