We introduce ShadowGAN, a generative adversarial network (GAN) for synthesizing shadows for virtual objects inserted in images. Given a target image containing several existing objects with shadows, and an input source object with a specified insertion position, the network generates a realistic shadow for the source object. The shadow is synthesized by a generator; using the proposed local adversarial and global adversarial discriminators, the synthetic shadow’s appearance is locally realistic in shape, and globally consistent with other objects’ shadows in terms of shadow direction and area. To overcome the lack of training data, we produced training samples based on public 3D models and rendering technology. Experimental results from a user study show that the synthetic shadowed results look natural and authentic.

中文翻译：

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ShadowGAN：具有条件对抗网络的虚拟对象的阴影合成

我们介绍ShadowGAN，一种生成对抗网络（GAN），用于为插入图像中的虚拟对象合成阴影。给定一个目标图像，其中包含几个带有阴影的现有对象，以及一个具有指定插入位置的输入源对象，网络将为该源对象生成逼真的阴影。阴影由生成器合成；使用提议的局部对抗和全局对抗区分符，合成阴影的外观形状局部真实，并且在阴影方向和区域方面与其他对象的阴影全局一致。为了克服训练数据的不足，我们基于公共3D模型和渲染技术制作了训练样本。用户研究的实验结果表明，合成阴影效果看起来自然而真实。