Composite Image

Composite image generation aims to seamlessly integrate foreground objects into background images, creating realistic and visually harmonious results. Current research focuses on improving the realism of composite images by addressing challenges like shadow generation, maintaining foreground detail, and handling semantic discrepancies between foreground and background, often employing diffusion models, transformers, and adversarial learning within various network architectures. These advancements have significant implications for applications such as photo editing, virtual try-on, and autonomous driving, improving the quality and efficiency of image manipulation and scene understanding.