MAEPose: Self-Supervised Spatiotemporal Learning for Human Pose Estimation on mmWave Video
Millimetre-wave (mmWave) radar offers a more privacy-preserving alternative to RGB-based human pose estimation. However, existing methods typically rely on pre-extracted intermediate representations such as sparse point clouds or spectrogram images, where the rich spatiotemporal information naturally present in radar video streams is discarded for model learning, while such signal processing adds system complexity. In addition, existing solutions are mainly conducted in an end-to-end supervised manner without leveraging unlabelled raw video streams to learn generalized representations. In this study, we present MAEPose, a masked autoencoding-based human pose estimation approach that opera...
| Authors | Xijia Wei, Yuan Fang, Kevin Chetty et al. |
|---|---|
| Primary category | cs.CV |
| Published | 30 April 2026 |
| Last updated at source | 3 June 2026 |
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