EgoBody: Human Body Shape and Motion of Interacting People from Head-Mounted Devices

TL;DR

EgoBody introduces a large-scale egocentric dataset capturing human body shape and motion during interactions, enabling improved 3D pose estimation from head-mounted device data.

Contribution

The paper presents EgoBody, a novel dataset with high-quality annotations for 3D human pose and shape estimation from egocentric views during social interactions.

Findings

State-of-the-art methods show limitations in egocentric scenarios.

High-quality annotations improve pose estimation accuracy.

The dataset enables new research in egocentric social interaction understanding.

Abstract

Understanding social interactions from egocentric views is crucial for many applications, ranging from assistive robotics to AR/VR. Key to reasoning about interactions is to understand the body pose and motion of the interaction partner from the egocentric view. However, research in this area is severely hindered by the lack of datasets. Existing datasets are limited in terms of either size, capture/annotation modalities, ground-truth quality, or interaction diversity. We fill this gap by proposing EgoBody, a novel large-scale dataset for human pose, shape and motion estimation from egocentric views, during interactions in complex 3D scenes. We employ Microsoft HoloLens2 headsets to record rich egocentric data streams (including RGB, depth, eye gaze, head and hand tracking). To obtain accurate 3D ground truth, we calibrate the headset with a multi-Kinect rig and fit expressive SMPL-X body meshes to multi-view RGB-D frames, reconstructing 3D human shapes and poses relative to the scene, over time. We collect 125 sequences, spanning diverse interaction scenarios, and propose the first benchmark for 3D full-body pose and shape estimation of the social partner from egocentric views. We extensively evaluate state-of-the-art methods, highlight their limitations in the egocentric scenario, and address such limitations leveraging our high-quality annotations. Data and code are available at https://sanweiliti.github.io/egobody/egobody.html.