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Advancements in graphics technology has increased the use of simulated datafor training machine learning models. However, the simulated data often differsfrom real-world data, creating a distribution gap that can decrease theefficacy of models trained on simulation data in real-world applications. Tomitigate this gap, sim-to-real domain transfer modifies simulated images tobetter match real-world data, enabling the effective use of simulation data inmodel training.  Sim-to-real transfer utilizes image translation methods, which are dividedinto two main categories: paired and unpaired image-to-image translation.Paired image translation requires a perfect pixel match, making it difficult toapply in practice due to the lack of pixel-wise correspondence betweensimulation and real-world data. Unpaired image translation, while more suitablefor sim-to-real transfer, is still challenging to learn for complex naturalscenes. To address these challenges, we propose a third category:approximately-paired sim-to-real translation, where the source and targetimages do not need to be exactly paired. Our approximately-paired method,AptSim2Real, exploits the fact that simulators can generate scenes looselyresembling real-world scenes in terms of lighting, environment, andcomposition. Our novel training strategy results in significant qualitative andquantitative improvements, with up to a 24% improvement in FID score comparedto the state-of-the-art unpaired image-translation methods.

AptSim2Real: Approximately-Paired Sim-to-Real Image Translation