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We propose a method that augments a simulated dataset using diffusion modelsto improve the performance of pedestrian detection in real-world data. The highcost of collecting and annotating data in the real-world has motivated the useof simulation platforms to create training datasets. While simulated data isinexpensive to collect and annotate, it unfortunately does not always closelymatch the distribution of real-world data, which is known as the sim2real gap.In this paper we propose a novel method of synthetic data creation meant toclose the sim2real gap for the challenging pedestrian detection task. Ourmethod uses a diffusion-based architecture to learn a real-world distributionwhich, once trained, is used to generate datasets. We mix this generated datawith simulated data as a form of augmentation and show that training on acombination of generated and simulated data increases average precision by asmuch as 27.3% for pedestrian detection models in real-world data, comparedagainst training on purely simulated data.

Diffusion Dataset Generation: Towards Closing the Sim2Real Gap for Pedestrian Detection