We introduce a novel human pose estimation benchmark with millimeter wave radar that includes synchronized vision and radio signal components. This dataset is created using cross-calibrated mmWave radar sensors and a monocular RGB camera for cross-modality training of radar-based human pose estimation. There are two advantages of using mmWave radar to perform human pose estimation. First, it is robust to dark and low-light conditions. Second, it is not visually perceivable by humans and therefore, can be widely applied to applications with privacy concerns. In addition to the benchmark, we propose a cross-modality training framework that leverages the ground-truth keypoints representing human body joints for training, which are systematically generated from the pre-trained pose estimation network based on a monocular camera input image, avoiding laborious manual annotation efforts.