ADAPTIVE AND ROBUST MMWAVE-BASED 3D HUMAN MESH ESTIMATION FOR DIVERSE POSES

This paper proposes a three-dimensional (3D) human mesh estimation framework with only a single commercial portable millimeter-wave device. Perceiving a 3D human mesh that includes poses and body shapes of a person with such a simple setting has remarkable potential for various applications such as daily activity monitoring and motion analysis for sports enhancement. Due to estimation difficulties, given a noisy input that includes signals reflected from the other person or objects in addition to the target person, most existing studies implicitly assume that the person stands almost vertically or that there is nothing else to be observed. Since such situations are unlikely to occur in real life, there is an urgent need for a more practical method. Therefore, we propose a framework that has the ability to extract only signals reflected from the target person and obtain local features that flexibly fit various poses that humans can take, including horizontal postures, such as lying. Our experiments suggest that the framework works effectively and that our method outperforms the baseline method.