Temel Kavramlar
mmDiff proposes a diffusion-based pose estimator tailored for noisy radar data, achieving state-of-the-art performance in human pose estimation.
Özet
The content introduces mmDiff, a novel diffusion-based pose estimator designed for noisy radar data. It addresses challenges in human pose estimation using mmWave radar technology. The article discusses the proposed approach, key modules, experiments demonstrating superior performance, and comparisons with existing methods.
Directory:
- Introduction
- Human Pose Estimation Importance and Challenges.
- Diffusion Model for HPE
- Overview of Diffusion Models and their Application.
- Global-local Radar Context Modules
- GRC and LRC for robust feature extraction from radar point clouds.
- Structural-motion Consistent Patterns Modules
- SLC and TMC to ensure structural and motion consistency in pose estimation.
- Experiments on mmBody Dataset
- Performance evaluation of mmDiff compared to existing methods.
- Experiments on mm-Fi Dataset
- Generalizability analysis of mmDiff on a larger-scale dataset.
- Analytics and Efficiency Analysis
- Ablation studies, effectiveness of modules, model efficiency comparison.
İstatistikler
"Extensive experiments demonstrate that mmDiff outperforms existing methods significantly."
"Our approach achieves state-of-the-art performances on public datasets."
Alıntılar
"Operated at the frequencies of 30-300 GHz, commercial mmWave radars transmit and receive RF signals that penetrate human targets and occlusions."
"Inspired by such capability, we aim to mitigate the noise of mmWave HPE, which motivates mmDiff."