# mmJoints: Expanding Joint Representations Beyond (x,y,z) in mmWave-Based 3D Pose Estimation

arXiv: https://arxiv.org/abs/2510.08970 · HTML: https://mlsanity.com/paper/2510.08970/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 60 references that are in the map.
- First posted on arXiv 10 Oct 2025.

## Builds on (most cited first, 10 of 10)

- [Monocular 3D Human Pose Estimation In The Wild Using Improved CNN Supervision](https://mlsanity.com/paper/1611.09813/) (2016)
- [mm-Pose: Real-Time Human Skeletal Posture Estimation using mmWave Radars and CNNs](https://mlsanity.com/paper/1911.09592/) (2019)
- [Orthogonal Projection Loss](https://mlsanity.com/paper/2103.14021/) (2021)
- [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)
- [Fast and Scalable Human Pose Estimation using mmWave Point Cloud](https://mlsanity.com/paper/2205.00097/) (2022)
- [Towards Domain-Independent and Real-Time Gesture Recognition Using mmWave Signal](https://mlsanity.com/paper/2111.06195/) (2021)
- [A Survey of mmWave-based Human Sensing: Technology, Platform and Applications](https://mlsanity.com/paper/2308.03149/) (2023)
- [SUPER: Seated Upper Body Pose Estimation using mmWave Radars](https://mlsanity.com/paper/2407.02455/) (2024)
- [Interpersonal Distance Tracking with mmWave Radar and IMUs](https://mlsanity.com/paper/2303.12798/) (2023)

## Built on by (most cited first, 1 of 1)

- [Wave2Body: Rethinking mmWave Human Pose Estimation as Radar-to-Body Token Translation](https://mlsanity.com/paper/2607.18875/) (2026)

## Abstract (arXiv)

In mmWave-based pose estimation, sparse signals and weak reflections often cause models to infer body joints from statistical priors rather than sensor data. While prior knowledge helps in learning meaningful representations, over-reliance on it degrades performance in downstream tasks like gesture and activity recognition. In this paper, we introduce mmJoints, a framework that augments a pre-trained, black-box mmWave-based 3D pose estimator's output with additional joint descriptors. Rather than mitigating bias, mmJoints makes it explicit by estimating the likelihood of a joint being sensed and the reliability of its predicted location. These descriptors enhance interpretability and improve downstream task accuracy. Through extensive evaluations using over 115,000 signal frames across 13 pose estimation settings, we show that mmJoints estimates descriptors with an error rate below 4.2%. mmJoints also improves joint position accuracy by up to 12.5% and boosts activity recognition by up to 16% over state-of-the-art methods.