  This technical report introduces our solution, MEEV, proposed to the EgoBody
Challenge at ECCV 2022. Captured from head-mounted devices, the dataset
consists of human body shape and motion of interacting people. The EgoBody
dataset has challenges such as occluded body or blurry image. In order to
overcome the challenges, MEEV is designed to exploit multiscale features for
rich spatial information. Besides, to overcome the limited size of dataset, the
model is pre-trained with the dataset aggregated 2D and 3D pose estimation
datasets. Achieving 82.30 for MPJPE and 92.93 for MPVPE, MEEV has won the
EgoBody Challenge at ECCV 2022, which shows the effectiveness of the proposed
method. The code is available at https://github.com/clovaai/meev
