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Found 2 papers, 1 papers with code
PeopleSansPeople: A Synthetic Data Generator for Human-Centric Computer Vision
We found that pre-training a network using synthetic data and fine-tuning on various sizes of real-world data resulted in a keypoint AP increase of $+38. 03$ ($44. 43 \pm 0. 17$ vs. $6. 40$) for few-shot transfer (limited subsets of COCO-person train [2]), and an increase of $+1. 47$ ($63. 47 \pm 0. 19$ vs. $62. 00$) for abundant real data regimes, outperforming models trained with the same real data alone.
Unity Perception: Generate Synthetic Data for Computer Vision
We introduce the Unity Perception package which aims to simplify and accelerate the process of generating synthetic datasets for computer vision tasks by offering an easy-to-use and highly customizable toolset.
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