Understanding social interactions from first-person views is crucial for many applications, ranging from assistive robotics to AR/VR. A first step for reasoning about interactions is to understand human pose and shape. However, research in this area is currently hindered by the lack of data. Existing datasets are limited in terms of either size, annotations, ground-truth capture modalities or the diversity of interactions. We address this shortcoming by proposing EgoBody, a novel large-scale dataset for social 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 poses and shapes relative to the scene. We collect 68 sequences, spanning diverse sociological interaction categories, and propose the first benchmark for 3D full-body pose and shape estimation from egocentric views.