  Due to the difficulty of acquiring large-scale 3D human keypoint annotation,
previous methods for 3D human pose estimation (HPE) have often relied on 2D
image features and sequential 2D annotations. Furthermore, the training of
these networks typically assumes the prediction of a human bounding box and the
accurate alignment of 3D point clouds with 2D images, making direct application
in real-world scenarios challenging. In this paper, we present the 1st
framework for end-to-end 3D human pose estimation, named LPFormer, which uses
only LiDAR as its input along with its corresponding 3D annotations. LPFormer
consists of two stages: firstly, it identifies the human bounding box and
extracts multi-level feature representations, and secondly, it utilizes a
transformer-based network to predict human keypoints based on these features.
Our method demonstrates that 3D HPE can be seamlessly integrated into a strong
LiDAR perception network and benefit from the features extracted by the
network. Experimental results on the Waymo Open Dataset demonstrate the
state-of-the-art performance, and improvements even compared to previous
multi-modal solutions.
