Tapor: 3D Hand Pose Reconstruction with Fully Passive Thermal Sensing for Around-device Interactions

This paper presents the design and implementation of Tapor, a privacy-preserving, non-contact, and fully passive sensing system for accurate and robust 3D hand pose reconstruction for around-device interaction using a single low-cost thermal array sensor. Thermal sensing using inexpensive and miniature thermal arrays emerges with an excellent utility-privacy balance, offering an imaging resolution significantly lower than cameras but far superior to RF signals like radar or WiFi. The design of Tapor, however, is challenging, mainly because the captured temperature maps are low-resolution and textureless. To overcome the challenges, we investigate the thermo-depth and thermo-pose properties and present a novel physics-inspired neural network design that learns effective 3D spatial representations of potential hand poses. We then formulate the 3D pose reconstruction problem as a distinct retrieval task, enabling precise determination of the hand pose corresponding to the input temperature map. To deploy Tapor on IoT devices, we introduce an effective heterogeneous knowledge distillation method that reduces the computation by 377x. We fully implement Tapor and conduct comprehensive experiments in various real-world scenarios. The results demonstrate the remarkable performance of Tapor, which is further illustrated by four case studies of gesture control and finger tracking. We envision Tapor to be a ubiquitous interface for around-device control and have released the dataset, software, firmware, and demo videos at https://github.com/IOT-Tapor/TAPOR.