# AdaPose: Towards Cross-Site Device-Free Human Pose Estimation with Commodity WiFi

arXiv: https://arxiv.org/abs/2309.16964 · HTML: https://mlsanity.com/paper/2309.16964/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 52 references that are in the map.
- First posted on arXiv 29 Sep 2023.

## Builds on (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Unsupervised Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1409.7495/) (2014)
- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [Deep Domain Confusion: Maximizing for Domain Invariance](https://mlsanity.com/paper/1412.3474/) (2014)
- [DeepPose: Human Pose Estimation via Deep Neural Networks](https://mlsanity.com/paper/1312.4659/) (2013)
- [Learning Invariant Representations and Risks for Semi-supervised Domain Adaptation](https://mlsanity.com/paper/2010.04647/) (2020)
- [Person-in-WiFi: Fine-grained Person Perception using WiFi](https://mlsanity.com/paper/1904.00276/) (2019)
- [SenseFi: A Library and Benchmark on Deep-Learning-Empowered WiFi Human Sensing](https://mlsanity.com/paper/2207.07859/) (2022)
- [MetaFi: Device-Free Pose Estimation via Commodity WiFi for Metaverse Avatar Simulation](https://mlsanity.com/paper/2208.10414/) (2022)
- [Can WiFi Estimate Person Pose?](https://mlsanity.com/paper/1904.00277/) (2019)
- [AirFi: Empowering WiFi-based Passive Human Gesture Recognition to Unseen Environment via Domain Generalization](https://mlsanity.com/paper/2209.10285/) (2022)
- [SiFall: Practical Online Fall Detection with RF Sensing](https://mlsanity.com/paper/2301.03773/) (2023)
- [GaitFi: Robust Device-Free Human Identification via WiFi and Vision Multimodal Learning](https://mlsanity.com/paper/2208.14326/) (2022)
- [From Point to Space: 3D Moving Human Pose Estimation Using Commodity WiFi](https://mlsanity.com/paper/2012.14066/) (2020)
- [Unsupervised Domain Adaptation for RF-based Gesture Recognition](https://mlsanity.com/paper/2111.10602/) (2021)

## Built on by (most cited first, 12 of 12)

- [TENT: Connect Language Models with IoT Sensors for Zero-Shot Activity Recognition](https://mlsanity.com/paper/2311.08245/) (2023)
- [Diffusion Model is a Good Pose Estimator from 3D RF-Vision](https://mlsanity.com/paper/2403.16198/) (2024)
- [A Survey on Wi-Fi Sensing Generalizability: Taxonomy, Techniques, Datasets, and Future Research Prospects](https://mlsanity.com/paper/2503.08008/) (2025)
- [M4Human: A Large-Scale Multimodal mmWave Radar Benchmark for Human Mesh Reconstruction](https://mlsanity.com/paper/2512.12378/) (2025)
- [Towards Robust and Realistic Human Pose Estimation via WiFi Signals](https://mlsanity.com/paper/2501.09411/) (2025)
- [VST-Pose: A Velocity-Integrated Spatiotem-poral Attention Network for Human WiFi Pose Estimation](https://mlsanity.com/paper/2507.09672/) (2025)
- [Zero-Shot Open-Vocabulary Human Motion Grounding with Test-Time Training](https://mlsanity.com/paper/2511.15379/) (2025)
- [TinySense: Effective CSI Compression for Scalable and Accurate Wi-Fi Sensing](https://mlsanity.com/paper/2601.15838/) (2026)
- [Spectrum Shortage for Radio Sensing? Leveraging Ambient 5G Signals for Human Activity Detection](https://mlsanity.com/paper/2603.03579/) (2026)
- [RePos: Relative-to-Absolute Pose Factorization for Cross-Environment WiFi-Based 3D Human Pose Estimation](https://mlsanity.com/paper/2607.02986/) (2026)
- [KOALA: Koopman Operator Learning for WiFi-Based Anticipatory Hum](https://mlsanity.com/paper/2608.15815/) (2026)
- [Quantum-Assisted Memory-Efficient Training for Parameter-Intensive Wi-Fi-Based Human Activity Recognition](https://mlsanity.com/paper/2609.04271/) (2026)

## Abstract (arXiv)

WiFi-based pose estimation is a technology with great potential for the development of smart homes and metaverse avatar generation. However, current WiFi-based pose estimation methods are predominantly evaluated under controlled laboratory conditions with sophisticated vision models to acquire accurately labeled data. Furthermore, WiFi CSI is highly sensitive to environmental variables, and direct application of a pre-trained model to a new environment may yield suboptimal results due to domain shift. In this paper, we proposes a domain adaptation algorithm, AdaPose, designed specifically for weakly-supervised WiFi-based pose estimation. The proposed method aims to identify consistent human poses that are highly resistant to environmental dynamics. To achieve this goal, we introduce a Mapping Consistency Loss that aligns the domain discrepancy of source and target domains based on inner consistency between input and output at the mapping level. We conduct extensive experiments on domain adaptation in two different scenes using our self-collected pose estimation dataset containing WiFi CSI frames. The results demonstrate the effectiveness and robustness of AdaPose in eliminating domain shift, thereby facilitating the widespread application of WiFi-based pose estimation in smart cities.