Semantic-shape Adaptive Feature Modulation for Semantic Image Synthesis

Zhengyao Lv, Xiaoming Li, Zhenxing Niu, Bing Cao, Wangmeng Zuo

Abstract: Recent years have witnessed substantial progress in se-mantic image synthesis, it is still challenging in synthesizing photo-realistic images with rich details. Most previ-ous methods focus on exploiting the given semantic map, which just captures an object-level layout for an image. Obviously, a fine-grained part-level semantic layout will benefit object details generation, and it can be roughly in-ferred from an object's shape. In order to exploit the part-level layouts, we propose a Shape-aware Position Descrip-tor (SPD) to describe each pixel's positional feature, where object shape is explicitly encoded into the SP D feature. Fur-thermore, a Semantic-shape Adaptive Feature Modulation (SAFM) block is proposed to combine the given semantic map and our positional features to produce adaptively mod-ulated features. Extensive experiments demonstrate that the proposed SPD and SAFM significantly improve the gener-ation of objects with rich details. Moreover, our method performs favorably against the SOTA methods in terms of quantitative and qualitative evaluation. The source code and model are available at SAFM.