# SUPER: Seated Upper Body Pose Estimation using mmWave Radars

arXiv: https://arxiv.org/abs/2407.02455 · HTML: https://mlsanity.com/paper/2407.02455/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 44 references that are in the map.
- First posted on arXiv 2 Jul 2024.

## Builds on (most cited first, 14 of 14)

- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [On the Continuity of Rotation Representations in Neural Networks](https://mlsanity.com/paper/1812.07035/) (2018)
- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation](https://mlsanity.com/paper/2204.12484/) (2022)
- [Deep Learning-Based Human Pose Estimation: A Survey](https://mlsanity.com/paper/2012.13392/) (2020)
- [STAR: Sparse Trained Articulated Human Body Regressor](https://mlsanity.com/paper/2008.08535/) (2020)
- [mm-Pose: Real-Time Human Skeletal Posture Estimation using mmWave Radars and CNNs](https://mlsanity.com/paper/1911.09592/) (2019)
- [IMUPoser: Full-Body Pose Estimation using IMUs in Phones, Watches, and Earbuds](https://mlsanity.com/paper/2304.12518/) (2023)
- [Deep Inertial Poser: Learning to Reconstruct Human Pose from Sparse Inertial Measurements in Real Time](https://mlsanity.com/paper/1810.04703/) (2018)
- [mmBody Benchmark: 3D Body Reconstruction Dataset and Analysis for Millimeter Wave Radar](https://mlsanity.com/paper/2209.05070/) (2022)
- [HuPR: A Benchmark for Human Pose Estimation Using Millimeter Wave Radar](https://mlsanity.com/paper/2210.12564/) (2022)
- [DensePose From WiFi](https://mlsanity.com/paper/2301.00250/) (2022)
- [Pi-ViMo: Physiology-inspired Robust Vital Sign Monitoring using mmWave Radars](https://mlsanity.com/paper/2303.13816/) (2023)

## Built on by (most cited first, 5 of 5)

- [mmEgoHand: Egocentric Hand Pose Estimation and Gesture Recognition with Head-mounted Millimeter-wave Radar and IMU](https://mlsanity.com/paper/2501.13805/) (2025)
- [mmJoints: Expanding Joint Representations Beyond (x,y,z) in mmWave-Based 3D Pose Estimation](https://mlsanity.com/paper/2510.08970/) (2025)
- [Understanding the Representation of Older Adults in Motion Capture Locomotion Datasets](https://mlsanity.com/paper/2511.11713/) (2025)
- [RF-HOI: Recognize Human-Object Interaction with Radio Frequency Signals](https://mlsanity.com/paper/2608.00289/) (2026)
- [Learning Biomechanically Plausible Human Motion from Sparse Radar Point Clouds](https://mlsanity.com/paper/2608.03637/) (2026)

## Abstract (arXiv)

In industrial countries, adults spend a considerable amount of time sedentary each day at work, driving and during activities of daily living. Characterizing the seated upper body human poses using mmWave radars is an important, yet under-studied topic with many applications in human-machine interaction, transportation and road safety. In this work, we devise SUPER, a framework for seated upper body human pose estimation that utilizes dual-mmWave radars in close proximity. A novel masking algorithm is proposed to coherently fuse data from the radars to generate intensity and Doppler point clouds with complementary information for high-motion but small radar cross section areas (e.g., upper extremities) and low-motion but large RCS areas (e.g. torso). A lightweight neural network extracts both global and local features of upper body and output pose parameters for the Skinned Multi-Person Linear (SMPL) model. Extensive leave-one-subject-out experiments on various motion sequences from multiple subjects show that SUPER outperforms a state-of-the-art baseline method by 30 -- 184%. We also demonstrate its utility in a simple downstream task for hand-object interaction.