  Human Pose Estimation (HPE) based on RGB images has experienced a rapid
development benefiting from deep learning. However, event-based HPE has not
been fully studied, which remains great potential for applications in extreme
scenes and efficiency-critical conditions. In this paper, we are the first to
estimate 2D human pose directly from 3D event point cloud. We propose a novel
representation of events, the rasterized event point cloud, aggregating events
on the same position of a small time slice. It maintains the 3D features from
multiple statistical cues and significantly reduces memory consumption and
computation complexity, proved to be efficient in our work. We then leverage
the rasterized event point cloud as input to three different backbones,
PointNet, DGCNN, and Point Transformer, with two linear layer decoders to
predict the location of human keypoints. We find that based on our method,
PointNet achieves promising results with much faster speed, whereas Point
Transfomer reaches much higher accuracy, even close to previous
event-frame-based methods. A comprehensive set of results demonstrates that our
proposed method is consistently effective for these 3D backbone models in
event-driven human pose estimation. Our method based on PointNet with 2048
points input achieves 82.46mm in MPJPE3D on the DHP19 dataset, while only has a
latency of 12.29ms on an NVIDIA Jetson Xavier NX edge computing platform, which
is ideally suitable for real-time detection with event cameras. Code is
available at https://github.com/MasterHow/EventPointPose.
