# Fast and Scalable Human Pose Estimation using mmWave Point Cloud

arXiv: https://arxiv.org/abs/2205.00097 · HTML: https://mlsanity.com/paper/2205.00097/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Apr 2022.

## Built on by (most cited first, 20 of 20)

- [MM-Fi: Multi-Modal Non-Intrusive 4D Human Dataset for Versatile Wireless Sensing](https://mlsanity.com/paper/2305.10345/) (2023)
- [mRI: Multi-modal 3D Human Pose Estimation Dataset using mmWave, RGB-D, and Inertial Sensors](https://mlsanity.com/paper/2210.08394/) (2022)
- [Diffusion Model is a Good Pose Estimator from 3D RF-Vision](https://mlsanity.com/paper/2403.16198/) (2024)
- [X-Fi: A Modality-Invariant Foundation Model for Multimodal Human Sensing](https://mlsanity.com/paper/2410.10167/) (2024)
- [Towards Balanced Multi-Modal Learning in 3D Human Pose Estimation](https://mlsanity.com/paper/2501.05264/) (2025)
- [M4Human: A Large-Scale Multimodal mmWave Radar Benchmark for Human Mesh Reconstruction](https://mlsanity.com/paper/2512.12378/) (2025)
- [Human Modelling and Pose Estimation Overview](https://mlsanity.com/paper/2406.19290/) (2024)
- [mmPred: Radar-based Human Motion Prediction in the Dark](https://mlsanity.com/paper/2512.00345/) (2025)
- [GenHPE: Generative Counterfactuals for 3D Human Pose Estimation with Radio Frequency Signals](https://mlsanity.com/paper/2503.09537/) (2025)
- [HoloLLM: Multisensory Foundation Model for Language-Grounded Human Sensing and Reasoning](https://mlsanity.com/paper/2505.17645/) (2025)
- [mmJoints: Expanding Joint Representations Beyond (x,y,z) in mmWave-Based 3D Pose Estimation](https://mlsanity.com/paper/2510.08970/) (2025)
- [Expanding mmWave Datasets for Human Pose Estimation with Unlabeled Data and LiDAR Datasets](https://mlsanity.com/paper/2603.14507/) (2026)
- [Wave2Body: Rethinking mmWave Human Pose Estimation as Radar-to-Body Token Translation](https://mlsanity.com/paper/2607.18875/) (2026)
- [PCFEx: Point Cloud Feature Extraction for Graph Neural Networks](https://mlsanity.com/paper/2603.08540/) (2026)
- [mmGAT: Pose Estimation by Graph Attention with Mutual Features from mmWave Radar Point Cloud](https://mlsanity.com/paper/2603.08551/) (2026)
- [Privacy-Preserving Industrial Ergonomics: mmWave-Based Automated REBA Scoring and Pose Estimation](https://mlsanity.com/paper/2607.02611/) (2026)
- [RePos: Relative-to-Absolute Pose Factorization for Cross-Environment WiFi-Based 3D Human Pose Estimation](https://mlsanity.com/paper/2607.02986/) (2026)
- [Learning Biomechanically Plausible Human Motion from Sparse Radar Point Clouds](https://mlsanity.com/paper/2608.03637/) (2026)
- [You Only Flow Once: Calibrated and Real-Time Radar Pose Estimation with Multi-Hypothesis Normalizing Flows](https://mlsanity.com/paper/2608.09579/) (2026)
- [Can Language Models Understand mmWave Data? Benchmarking Large Language Models for mmWave Radar-Based Human Understanding](https://mlsanity.com/paper/2608.14179/) (2026)

## Abstract (arXiv)

Millimeter-Wave (mmWave) radar can enable high-resolution human pose estimation with low cost and computational requirements. However, mmWave data point cloud, the primary input to processing algorithms, is highly sparse and carries significantly less information than other alternatives such as video frames. Furthermore, the scarce labeled mmWave data impedes the development of machine learning (ML) models that can generalize to unseen scenarios. We propose a fast and scalable human pose estimation (FUSE) framework that combines multi-frame representation and meta-learning to address these challenges. Experimental evaluations show that FUSE adapts to the unseen scenarios 4× faster than current supervised learning approaches and estimates human joint coordinates with about 7 cm mean absolute error.