# Construct 3D Hand Skeleton with Commercial WiFi

arXiv: https://arxiv.org/abs/2312.15507 · HTML: https://mlsanity.com/paper/2312.15507/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Dec 2023.

## Built on by (most cited first, 10 of 10)

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- [Compressing Deep Reinforcement Learning Networks with a Dynamic Structured Pruning Method for Autonomous Driving](https://mlsanity.com/paper/2402.05146/) (2024)
- [Breaking Coordinate Overfitting: Geometry-Aware WiFi Sensing for Cross-Layout 3D Pose Estimation](https://mlsanity.com/paper/2601.12252/) (2026)
- [GenHPE: Generative Counterfactuals for 3D Human Pose Estimation with Radio Frequency Signals](https://mlsanity.com/paper/2503.09537/) (2025)
- [Towards Seeing Bones at Radio Frequency](https://mlsanity.com/paper/2509.17979/) (2025)
- [Scale What Counts, Mask What Matters: Evaluating Foundation Models for Zero-Shot Cross-Domain Wi-Fi Sensing](https://mlsanity.com/paper/2511.18792/) (2025)
- [FuseFi: Combining Irregularly Sampled CSI from Diverse Communication Packets and Frequency Bands for Wi-Fi Sensing](https://mlsanity.com/paper/2512.22143/) (2025)
- [NeuroPath: Practically Adopting Motor Imagery Decoding through EEG Signals](https://mlsanity.com/paper/2604.09654/) (2026)

## Abstract (arXiv)

This paper presents HandFi, which constructs hand skeletons with practical WiFi devices. Unlike previous WiFi hand sensing systems that primarily employ predefined gestures for pattern matching, by constructing the hand skeleton, HandFi can enable a variety of downstream WiFi-based hand sensing applications in gaming, healthcare, and smart homes. Deriving the skeleton from WiFi signals is challenging, especially because the palm is a dominant reflector compared with fingers. HandFi develops a novel multi-task learning neural network with a series of customized loss functions to capture the low-level hand information from WiFi signals. During offline training, HandFi takes raw WiFi signals as input and uses the leap motion to provide supervision. During online use, only with commercial WiFi, HandFi is capable of producing 2D hand masks as well as 3D hand poses. We demonstrate that HandFi can serve as a foundation model to enable developers to build various applications such as finger tracking and sign language recognition, and outperform existing WiFi-based solutions. Artifacts can be found: https://github.com/SIJIEJI/HandFi