Human pose estimation (HPE) is a crucial task in computer vision with extensive applications in healthcare, surveillance, and human–computer interaction. Traditional HPE research primarily utilizes RGB cameras, which may suffer from poor performance under varying lighting conditions and raise privacy concerns. Recently, millimeter-wave (mmWave) radar technology has emerged as a promising alternative, providing a non-invasive and privacy-preserving solution for HPE. However, the progress in mmWave-based HPE is hindered by the limited availability of high-quality datasets that encompass a diverse range of poses and provide accurate data annotations. Current mmWave-based datasets for HPE often feature only basic poses or rely on imprecise annotations, typically derived from pre-trained image-based HPE models using synchronized RGB images, which can limit the potential of derived models. This study introduces a pioneering approach to HPE by synergizing wearable motion capture sensors with mmWave radar technology to create a comprehensive and precise dataset tailored for enhancing HPE with mmWave radar. Leveraging this dataset, we develop an innovative deep learning framework specifically designed to explore the unique properties of radar signals for HPE. The performance of our proposed model is evaluated and compared with several well-known deep learning models. Extensive experimental results affirm the robustness of the dataset, establishing it as a rigorous benchmark for mmWave radar-based HPE. The proposed methodology demonstrates exceptional accuracy in estimating human poses from radar data, setting the stage for its application in environments where privacy and complexity are critical concerns.