  This paper presents a novel 3D human pose estimation approach using a single
stream of asynchronous events as input. Most of the state-of-the-art approaches
solve this task with RGB cameras, however struggling when subjects are moving
fast. On the other hand, event-based 3D pose estimation benefits from the
advantages of event-cameras, especially their efficiency and robustness to
appearance changes. Yet, finding human poses in asynchronous events is in
general more challenging than standard RGB pose estimation, since little or no
events are triggered in static scenes. Here we propose the first learning-based
method for 3D human pose from a single stream of events. Our method consists of
two steps. First, we process the event-camera stream to predict three
orthogonal heatmaps per joint; each heatmap is the projection of of the joint
onto one orthogonal plane. Next, we fuse the sets of heatmaps to estimate 3D
localisation of the body joints. As a further contribution, we make available a
new, challenging dataset for event-based human pose estimation by simulating
events from the RGB Human3.6m dataset. Experiments demonstrate that our method
achieves solid accuracy, narrowing the performance gap between standard RGB and
event-based vision. The code is freely available at
https://iit-pavis.github.io/lifting_events_to_3d_hpe.
