SAC-GAN: Structure-Aware Image Composition

TL;DR

SAC-GAN introduces a structure-aware, end-to-end learning framework for image composition that emphasizes semantic and structural coherence over pixel-level accuracy, utilizing a self-supervised approach and adversarial training.

Contribution

The paper presents a novel structure-aware image composition method that integrates semantic layout features and a differentiable spatial transformer within an adversarial framework.

Findings

Outperforms state-of-the-art methods in image composition quality

Demonstrates strong generalizability across various scenarios

Effectively maintains semantic and structural coherence in composite images

Abstract

We introduce an end-to-end learning framework for image-to-image composition, aiming to plausibly compose an object represented as a cropped patch from an object image into a background scene image. As our approach emphasizes more on semantic and structural coherence of the composed images, rather than their pixel-level RGB accuracies, we tailor the input and output of our network with structure-aware features and design our network losses accordingly, with ground truth established in a self-supervised setting through the object cropping. Specifically, our network takes the semantic layout features from the input scene image, features encoded from the edges and silhouette in the input object patch, as well as a latent code as inputs, and generates a 2D spatial affine transform defining the translation and scaling of the object patch. The learned parameters are further fed into a differentiable spatial transformer network to transform the object patch into the target image, where our model is trained adversarially using an affine transform discriminator and a layout discriminator. We evaluate our network, coined SAC-GAN, for various image composition scenarios in terms of quality, composability, and generalizability of the composite images. Comparisons are made to state-of-the-art alternatives, including Instance Insertion, ST-GAN, CompGAN and PlaceNet, confirming superiority of our method.