Abstract
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.
Highlights
Inserting virtual objects into scenes has a wide range of applications in visual media, from movies, advertisements, and entertainment to virtual reality
We address the shadow synthesis problem for virtual objects inserted in an image
Our proposed ShadowGAN is trained on synthetic data, where static scene images are rendered using 3D models indexed by ShapeNet [34]
Summary
Inserting virtual objects into scenes has a wide range of applications in visual media, from movies, advertisements, and entertainment to virtual reality. Consistency of shadows between the original scene and the inserted object contributes greatly to the naturalness of the results. Even an experienced editor spends much effort to produce convincing results using commercial editing software such as Adobe Photoshop. The difficulties in this process stem from the lack of accurate estimates of illumination and scene geometry. Other methods [1,2,3,4] synthesize shadows with approximately estimated illumination and reconstructed scene geometry. Such computations either require user interaction or precise tools, and yet are time-consuming
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