Image Synthesis
Image synthesis focuses on generating realistic images from various inputs, such as text descriptions, sketches, or other images, aiming to improve controllability, realism, and efficiency. Current research emphasizes advancements in diffusion models, generative adversarial networks (GANs), and autoregressive models, often incorporating techniques like latent space manipulation, multimodal conditioning (text and image), and attention mechanisms to enhance image quality and control. This field is significant for its applications in diverse areas, including medical imaging, virtual try-ons, and content creation, while also raising important considerations regarding ethical implications and environmental impact of computationally intensive models.
Papers
Reconciling Semantic Controllability and Diversity for Remote Sensing Image Synthesis with Hybrid Semantic Embedding
Junde Liu, Danpei Zhao, Bo Yuan, Wentao Li, Tian Li
Cross Group Attention and Group-wise Rolling for Multimodal Medical Image Synthesis
Tao Song, Yicheng Wu, Minhao Hu, Xiangde Luo, Linda Wei, Guotai Wang, Yi Guo, Feng Xu, Shaoting Zhang