Abstract
In this paper, we present a novel approach to synthesize realistic images based on their semantic layouts. The core idea is that for objects with similar appearance, they also share similar representation. To this end, we first regress semantic layouts to natural images, with intermediate representation that contains both semantic and appearance information. Then we propose a dynamic weighted network that takes these intermediate representations as a condition to generate high-quality results. We demonstrate that our method gives the compelling generation performance qualitatively and quantitatively with extensive experiments on benchmarks.
Ying-Cong Chen
Assistant Professor
Ying-Cong Chen is an Assistant Professor at AI Thrust, Information Hub of Hong Kong University of Science and Technology (Guangzhou Campus). He obtained his Ph.D. degree from the Chinese University of Hong Kong. His research lies in the broad area of computer vision and machine learning, aiming for empowering machine with the capacity to understand human appearance, physiology and psychology. His works contribute to a wide range of applications, including contactless health monitoring, semantic photo synthesis, and intelligent video surveillance.