SSGAN: Image Generation from Freehand Scene Sketches

Abstract:
With the remarkable progress on deep CNNs, recent approaches have achieved certain success on image generation from scene-level freehand sketches. However, most of the researches adopt a two-staged way, that is, to generate the foreground and the background of the image respectively. In this paper, we propose a novel one-stage paradigm of GAN-based architecture, which named SSGAN for image generation using sketch-to-image directly. Moreover, we design a novel Semantic Fusion Module (SFM) for better learn the intermediate features. Extensive experiments on SketchyCOCO demonstrate that our proposed framework can obtain competitive performance compared with the state-of-the-art methods.