  Synthetic-to-real data translation using generative adversarial learning has
achieved significant success in improving synthetic data. Yet, limited studies
focus on deep evaluation and comparison of adversarial training on
general-purpose synthetic data for machine learning. This work aims to train
and evaluate a synthetic-to-real generative model that transforms the synthetic
renderings into more realistic styles on general-purpose datasets conditioned
with unlabeled real-world data. Extensive performance evaluation and comparison
have been conducted through qualitative and quantitative metrics and a defined
downstream perception task.
