Stable 3D Human Pose Estimation in Low- Resolution Videos with a Few Views

Abstract

We discuss the problem of 3D pose estimation for multi-view videos. With previous frame-by-frame multi-view methods, it has been difficult to achieve stable estimation under challenging settings such as low-resolution or with only a few views. Temporal approaches are effective ways of addressing such problems, but enforcing temporal consistency with neigh-boring frames sometimes damages the precision of the results. We propose a temporal approach with selective corrections based on the observation that errors in the frame-by-frame approach are concentrated under certain adverse conditions. Our method evaluates the confidence of the frame-by-frame results and compensates for the inaccurate keypoints with temporal information while retaining the accurate keypoints. In our experiments on the CMU Panoptic dataset customized for low-resolution and a few views, we reported 32.98 mm for MPJPE and 98.64% for 3D-PCK@150. Compared to the state-of-the-art method, our method improved MPJPE by 1.14 mm and corrected 16 % of incorrect keypoints.