Photorealistic Image

Photorealistic image generation aims to create highly realistic synthetic images, driven by applications ranging from virtual try-ons to autonomous vehicle training. Current research heavily utilizes diffusion models and generative adversarial networks (GANs), often incorporating techniques like neural radiance fields (NeRFs) for 3D scene representation and text-to-image prompting for controlled generation. These advancements are improving the quality and consistency of generated images, addressing challenges like prompt adherence and cross-population bias in training data, and impacting fields such as face recognition, fashion design, and earth sciences through improved data augmentation and simulation capabilities.