  Unconditional human image generation is an important task in vision and
graphics, which enables various applications in the creative industry. Existing
studies in this field mainly focus on "network engineering" such as designing
new components and objective functions. This work takes a data-centric
perspective and investigates multiple critical aspects in "data engineering",
which we believe would complement the current practice. To facilitate a
comprehensive study, we collect and annotate a large-scale human image dataset
with over 230K samples capturing diverse poses and textures. Equipped with this
large dataset, we rigorously investigate three essential factors in data
engineering for StyleGAN-based human generation, namely data size, data
distribution, and data alignment. Extensive experiments reveal several valuable
observations w.r.t. these aspects: 1) Large-scale data, more than 40K images,
are needed to train a high-fidelity unconditional human generation model with
vanilla StyleGAN. 2) A balanced training set helps improve the generation
quality with rare face poses compared to the long-tailed counterpart, whereas
simply balancing the clothing texture distribution does not effectively bring
an improvement. 3) Human GAN models with body centers for alignment outperform
models trained using face centers or pelvis points as alignment anchors. In
addition, a model zoo and human editing applications are demonstrated to
facilitate future research in the community.
