Image composition targets at inserting a foreground object on a background image. Most previous image composition methods focus on adjusting the foreground to make it compatible with background while ignoring the shadow effect of foreground on the background. In this work, we focus on generating plausible shadow for the foreground object in the composite image. First, we contribute a real-world shadow generation dataset DESOBA by generating synthetic composite images based on paired real images and deshadowed images. Then, we propose a novel shadow generation network SGRNet, which consists of a shadow mask prediction stage and a shadow filling stage. In the shadow mask prediction stage, foreground and background information are thoroughly interacted to generate foreground shadow mask. In the shadow filling stage, shadow parameters are predicted to fill the shadow area. Extensive experiments on our DESOBA dataset and real composite images demonstrate the effectiveness of our proposed method.

翻译：在背景图像上插入前景对象时, 图像的构成目标。 大多数先前的图像构成方法都侧重于调整前景, 使其与背景相容, 同时忽略前景对背景的阴影效应 。 在这项工作中, 我们侧重于为复合图像中的前景对象创造可信的影子 。 首先, 我们通过根据对齐真实图像和被冲淡的图像生成合成合成合成图像来贡献真实世界的影子生成数据集DESOBA 。 然后, 我们提议建立一个新型的影子生成网络 SGRNet, 由阴影遮罩预测阶段和阴影填充阶段组成。 在阴影遮罩预测阶段, 前景和背景信息将进行彻底互动, 以生成浅色阴影遮罩 。 在阴影填充阶段, 阴影参数将被预测来填补阴影区域 。 在 DESOBA 数据集和真实合成图像上进行广泛的实验, 展示我们拟议方法的有效性 。