  Following improvements in deep neural networks, state-of-the-art networks
have been proposed for human recognition using point clouds captured by LiDAR.
However, the performance of these networks strongly depends on the training
data. An issue with collecting training data is labeling. Labeling by humans is
necessary to obtain the ground truth label; however, labeling requires huge
costs. Therefore, we propose an automatic labeled data generation pipeline, for
which we can change any parameters or data generation environments. Our
approach uses a human model named Dhaiba and a background of Miraikan and
consequently generated realistic artificial data. We present 500k+ data
generated by the proposed pipeline. This paper also describes the specification
of the pipeline and data details with evaluations of various approaches.
