Deep Image Compositing
This work addresses the challenge of image compositing for people without professional editing skills, making it more accessible, though it appears incremental as it applies GANs to a known task.
The paper tackles the problem of making composite images look realistic by using Generative Adversarial Networks (GANs) to blend foreground and background objects, and it reports that their method outperforms all current state-of-the-art methods on their created dataset.
In image editing, the most common task is pasting objects from one image to the other and then eventually adjusting the manifestation of the foreground object with the background object. This task is called image compositing. But image compositing is a challenging problem that requires professional editing skills and a considerable amount of time. Not only these professionals are expensive to hire, but the tools (like Adobe Photoshop) used for doing such tasks are also expensive to purchase making the overall task of image compositing difficult for people without this skillset. In this work, we aim to cater to this problem by making composite images look realistic. To achieve this, we are using Generative Adversarial Networks (GANS). By training the network with a diverse range of filters applied to the images and special loss functions, the model is able to decode the color histogram of the foreground and background part of the image and also learns to blend the foreground object with the background. The hue and saturation values of the image play an important role as discussed in this paper. To the best of our knowledge, this is the first work that uses GANs for the task of image compositing. Currently, there is no benchmark dataset available for image compositing. So we created the dataset and will also make the dataset publicly available for benchmarking. Experimental results on this dataset show that our method outperforms all current state-of-the-art methods.