Denoising Diffusion Probabilistic Models (DDPMs) have achieved remarkable success in
various image generation tasks compared with Generative Adversarial Nets (GANs). Recent …

Typical layout-to-image synthesis (LIS) models generate images for a closed set of semantic
classes, eg, 182 common objects in COCO-Stuff. In this work, we explore the freestyle …

We propose a new framework for conditional image synthesis from semantic layouts of any
precision levels, ranging from pure text to a 2D semantic canvas with precise shapes. More …

Recent advancements in large-scale pre-trained text-to-image models have led to
remarkable progress in semantic image synthesis. Nevertheless synthesizing high-quality …

We propose a novel e dge guided g enerative a dversarial n etwork with c ontrastive
learning (ECGAN) for the challenging semantic image synthesis task. Although considerable …

We propose a novel ECGAN for the challenging semantic image synthesis task. Although
considerable improvement has been achieved, the quality of synthesized images is far from …

Semantic image synthesis approaches has been dominated by the modelling of
Convolutional Neural Networks (CNN). Due to the limitations of local perception, their …

Although the existing semantic image synthesis methods based on generative adversarial
networks (GANs) have achieved great success, the quality of the generated images still …

Generating photo-realistic remote sensing images conditioned on semantic masks has
many practical applications like image editing, detecting deep fake geography, and data …

Despite the progress in semantic image synthesis, it remains a challenging problem to
generate photo-realistic parts from input semantic map. Integrating part segmentation map …