  Since annotating pixel-level labels for semantic segmentation is laborious,
leveraging synthetic data is an attractive solution. However, due to the domain
gap between synthetic domain and real domain, it is challenging for a model
trained with synthetic data to generalize to real data. In this paper,
considering the fundamental difference between the two domains as the texture,
we propose a method to adapt to the texture of the target domain. First, we
diversity the texture of synthetic images using a style transfer algorithm. The
various textures of generated images prevent a segmentation model from
overfitting to one specific (synthetic) texture. Then, we fine-tune the model
with self-training to get direct supervision of the target texture. Our results
achieve state-of-the-art performance and we analyze the properties of the model
trained on the stylized dataset with extensive experiments.
