Automatically Extract Semantic Map for Semantic Style Transfer
ChangMing Wu, Min Yao · 2022 7th International Conference on Intelligent Computing and Signal Processing (ICSP) · 2022
As one of the fields of computer art creation, style transfer has become more and more popular. However, in order to obtain good visual effects, a large number of neural style transfer algorithms use semantic map to guide the style transfer between the correct regions. As an important means to ensure the quality of style transfer, semantic map can meaningfully control the results of style transfer. However, the method of manually generating semantic graph is cumbersome and inefficient. In this paper, we introduce a semantic segmentation network to automatically generate the semantic map required by neural style transfer, and combine it with neural style transfer network, we propose a new neural style transfer algorithm. Experiments show that our algorithm not only avoids cumbersome manual work, but also generates high-quality style transfer results.