PSP-HDRI$+$: A Synthetic Dataset Generator for Pre-Training of Human-Centric Computer Vision Models
This work addresses the need for improved pre-training data in computer vision, particularly for human-centric tasks, though it appears incremental as it builds on existing synthetic data methods.
The paper tackles the problem of pre-training human-centric computer vision models by introducing PSP-HDRI+, a synthetic data generator that outperforms ImageNet and other synthetic datasets, leading to better performance on out-of-distribution tests.
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.