Interactive Image Synthesis with Panoptic Layout Generation
Bo Wang, Tao Wu, Minfeng Zhu, Peng Du

TL;DR
This paper introduces PLGAN, a novel generative model that uses panoptic layout generation to improve interactive image synthesis, especially under imprecise user inputs, by separately modeling 'stuff' and 'things' for more realistic results.
Contribution
The paper proposes a new panoptic layout GAN that separately models 'stuff' and 'things' to handle imprecise inputs and fill missing regions, enhancing interactive image synthesis.
Findings
PLGAN outperforms state-of-the-art models on multiple datasets.
It produces more realistic and artifact-free images.
Quantitative metrics confirm its superior performance.
Abstract
Interactive image synthesis from user-guided input is a challenging task when users wish to control the scene structure of a generated image with ease.Although remarkable progress has been made on layout-based image synthesis approaches, in order to get realistic fake image in interactive scene, existing methods require high-precision inputs, which probably need adjustment several times and are unfriendly to novice users. When placement of bounding boxes is subject to perturbation, layout-based models suffer from "missing regions" in the constructed semantic layouts and hence undesirable artifacts in the generated images. In this work, we propose Panoptic Layout Generative Adversarial Networks (PLGAN) to address this challenge. The PLGAN employs panoptic theory which distinguishes object categories between "stuff" with amorphous boundaries and "things" with well-defined shapes, such…
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Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · Computer Graphics and Visualization Techniques · Advanced Vision and Imaging
