Person Image Generation

Person image generation focuses on creating realistic images of people, often manipulating pose, attributes, or context. Current research heavily utilizes diffusion models, often incorporating techniques like coarse-to-fine processing, disentangled representations, and attention mechanisms to improve image quality, controllability, and handling of complex transformations like pose transfer. This field is significant for its applications in virtual try-ons, digital avatars, and content creation, while also advancing our understanding of image synthesis and representation learning. The development of more efficient and robust models remains a key focus.