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We introduce an interactive Soft Shadow Network (SSN) to generates controllable soft shadows for image composit- ing. SSN takes a 2D object mask as input and thus is ag- nostic to image types such as painting and vector art. An environment light map is used to control the shadow’s char- acteristics, such as angle and softness. SSN employs an Ambient Occlusion Prediction module to predict an inter- mediate ambient occlusion map, which can be further re- fined by the user to provides geometric cues to modulate the shadow generation. To train our model, we design an efficient pipeline to produce diverse soft shadow training data using 3D object models. In addition, we propose an inverse shadow map representation to improve model train- ing. We demonstrate that our model produces realistic soft shadows in real-time. Our user studies show that the gen- erated shadows are often indistinguishable from shadows calculated by a physics-based renderer and users can eas- ily use SSN through an interactive application to generate specific shadow effects in minutes.