Shadow Generation for Composite Image in Real-world Scenes
Yan Hong, Li Niu, Jianfu Zhang, Liqing Zhang

TL;DR
This paper introduces a new dataset and a neural network for generating realistic shadows in composite images, improving visual plausibility in image editing.
Contribution
The work presents a novel shadow generation network and a real-world shadow dataset for enhanced composite image realism.
Findings
Effective shadow generation demonstrated on DESOBA dataset
Improved realism in composite images with generated shadows
Code and dataset publicly available for further research
Abstract
Image composition targets at inserting a foreground object into 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…
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Code & Models
Videos
Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · Face recognition and analysis · 3D Shape Modeling and Analysis
