We introduce the first method for automatic image generation from scene-level freehand sketches. Our model allows for controllable image generation by specifying the synthesis goal via freehand sketches. The key contribution is an attribute vector bridged generative adversarial network called edgeGAN which supports high visual-quality image content generation without using freehand sketches as training data. We build a large-scale composite dataset called SketchyCOCO to comprehensively evaluate our solution. We validate our approach on the task of both objectlevel and scene-level image generation on SketchyCOCO. We demonstrate the method's capacity to generate realistic complex scene-level images from a variety of freehand sketches by quantitative, qualitative results, and ablation studies.
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