  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 object-level image content generation without
using freehand sketches as training data. We have built a large-scale composite
dataset called SketchyCOCO to support and evaluate the solution. We validate
our approach on the tasks of both object-level and scene-level image generation
on SketchyCOCO. Through quantitative, qualitative results, human evaluation and
ablation studies, we demonstrate the method's capacity to generate realistic
complex scene-level images from various freehand sketches.
