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.
@article{arxiv.2207.05025,
title = {PSP-HDRI$+$: A Synthetic Dataset Generator for Pre-Training of Human-Centric Computer Vision Models},
author = {Salehe Erfanian Ebadi and Saurav Dhakad and Sanjay Vishwakarma and Chunpu Wang and You-Cyuan Jhang and Maciek Chociej and Adam Crespi and Alex Thaman and Sujoy Ganguly},
journal= {arXiv preprint arXiv:2207.05025},
year = {2022}
}
Comments
PSP-HDRI$+$ template Unity environment, benchmark binaries, and source code will be made available at: https://github.com/Unity-Technologies/PeopleSansPeople