Ego-Body Pose Estimation via Ego-Head Pose Estimation

Abstract

Estimating 3D human motion from an ego-centric video, which records the environment viewed from the first-person perspective with a front-facing monocular camera, is critical to applications in VR/AR. However, naively learning a mapping between egocentric videos and full-body human motions is challenging for two reasons. First, modeling this complex relationship is difficult; unlike reconstruction motion from third-person videos, the human body is often out of view of an egocentric video. Second, learning this mapping requires a large-scale, diverse dataset containing paired egocentric videos and the corresponding 3D human poses. Creating such a dataset requires meticulous instrumentation for data acquisition, and unfortunately, such a dataset does not currently exist. As such, existing works have only worked on small-scale datasets with limited motion and scene diversity (yuan20183d; yuan2019ego; luo2021dynamics).