  We introduce a new synthetic data generator PSP-HDRI$+$ that proves to be a
superior pre-training alternative to ImageNet and other large-scale synthetic
data counterparts. We demonstrate that pre-training with our synthetic data
will yield a more general model that performs better than alternatives even
when tested on out-of-distribution (OOD) sets. Furthermore, using ablation
studies guided by person keypoint estimation metrics with an off-the-shelf
model architecture, we show how to manipulate our synthetic data generator to
further improve model performance.
