Learning Layout and Style Reconfigurable GANs for Controllable Image Synthesis

TL;DR

This paper introduces a controllable image synthesis method that generates realistic images from spatial layouts and styles by learning object masks and applying style control at multiple levels, achieving state-of-the-art results.

Contribution

It proposes a novel layout-to-mask-to-image framework with style control at both image and object mask levels using a new normalization scheme, advancing controllable image synthesis.

Findings

Achieves state-of-the-art performance on COCO-Stuff and Visual Genome datasets.

Effectively controls object appearance and layout in generated images.

Demonstrates improved realism and diversity in synthesized images.

Abstract

With the remarkable recent progress on learning deep generative models, it becomes increasingly interesting to develop models for controllable image synthesis from reconfigurable inputs. This paper focuses on a recent emerged task, layout-to-image, to learn generative models that are capable of synthesizing photo-realistic images from spatial layout (i.e., object bounding boxes configured in an image lattice) and style (i.e., structural and appearance variations encoded by latent vectors). This paper first proposes an intuitive paradigm for the task, layout-to-mask-to-image, to learn to unfold object masks of given bounding boxes in an input layout to bridge the gap between the input layout and synthesized images. Then, this paper presents a method built on Generative Adversarial Networks for the proposed layout-to-mask-to-image with style control at both image and mask levels. Object masks are learned from the input layout and iteratively refined along stages in the generator network. Style control at the image level is the same as in vanilla GANs, while style control at the object mask level is realized by a proposed novel feature normalization scheme, Instance-Sensitive and Layout-Aware Normalization. In experiments, the proposed method is tested in the COCO-Stuff dataset and the Visual Genome dataset with state-of-the-art performance obtained.