Semantic Image Synthesis

Semantic image synthesis (SIS) aims to generate realistic images from semantic maps (e.g., segmentation masks), enabling precise control over image content and layout. Current research heavily utilizes generative adversarial networks (GANs) and diffusion models, often incorporating specialized architectures like spatially-adaptive normalization (SPADE) blocks and attention mechanisms to improve image quality and consistency with the input semantic information. This field is significant for applications such as data augmentation, sensor simulation, and creative content generation, impacting various domains including computer vision, medical imaging, and robotics.