We introduce an interactive Soft Shadow Network (SSN) to generates controllable soft shadows for image compositing. SSN takes a 2D object mask as input and thus is agnostic to image types such as painting and vector art. An environment light map is used to control the shadow's characteristics, such as angle and softness. SSN employs an Ambient Occlusion Prediction module to predict an intermediate ambient occlusion map, which can be further refined 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 training. We demonstrate that our model produces realistic soft shadows in real-time. Our user study shows that the generated shadows are often indistinguishable from shadows calculated by a physics-based renderer.

翻译：我们引入了一个互动软影阴影网络(SSN), 用于生成可控的软阴影, 用于图像合成。 SSN将 2D 对象遮罩作为输入, 因而对绘画和矢量艺术等图像类型具有不可知性。 使用环境光图来控制阴影的特性, 如角和软性。 SSN使用一个“ 环境封闭预测” 模块来预测一个中间环境封闭性地图, 用户可以对此进行进一步的改进, 以提供几何导线来调节阴影生成。 为了培训我们的模型, 我们设计了一个高效的管道, 以使用 3D 对象模型生成各种软影子培训数据。 此外, 我们提出一个反向的阴影映像来改进模型培训。 我们证明我们的模型实时产生现实的软阴影。 我们的用户研究表明, 生成的阴影往往与物理成型的阴影无法区分开来。