  Several methods have been proposed to estimate 3D human pose from multi-view
images, achieving satisfactory performance on public datasets collected under
relatively simple conditions. However, there are limited approaches studying
extracting 3D human skeletons from multimodal inputs, such as RGB and point
cloud data. To address this gap, we introduce LiCamPose, a pipeline that
integrates multi-view RGB and sparse point cloud information to estimate robust
3D human poses via single frame. We demonstrate the effectiveness of the
volumetric architecture in combining these modalities. Furthermore, to
circumvent the need for manually labeled 3D human pose annotations, we develop
a synthetic dataset generator for pretraining and design an unsupervised domain
adaptation strategy to train a 3D human pose estimator without manual
annotations. To validate the generalization capability of our method, LiCamPose
is evaluated on four datasets, including two public datasets, one synthetic
dataset, and one challenging self-collected dataset named BasketBall, covering
diverse scenarios. The results demonstrate that LiCamPose exhibits great
generalization performance and significant application potential. The code,
generator, and datasets will be made available upon acceptance of this paper.
