Semantic Image Synthesis via Hierarchical Structure Features

Semantic image synthesis, which converts semantic masks into photo-realistic images, is essentially a special form of a label-to-image task. In this area, previous work has made great progress, but we found that their models usually lose certain semantic information during the generation process, and the metrics of each generated result have a certain degree of fluctuation. So how to generate stable and high-quality images is still a challenge for this task. In this paper, we propose a Hierarchical Feature Block (HF-Block) from the perspective of improving the stability of generation. It generates different hierarchical features through a Hierarchical Feature Encoder (HF-Encoder) and merges them into the generator. We conducted extensive experiments on several very challenging datasets: ADE20K, Deepfashion, and Deepfashion2 datasets. Compared with the state-of-the-art methods, ours can provide more stable and high-quality images.