HyperHuman: Achieving Hyper-Realistic Human Image Generation

In the field of large-scale text-to-image models, achieving hyper-realistic human image generation has been a persistent challenge. Existing solutions, such as Stable Diffusion and DALL-E 2, often yield images with disjointed elements or unnatural poses. HyperHuman, an innovative approach developed by the team, addresses these issues directly. It leverages the inherent structural nature of human images across multiple scales, from the overarching body skeleton to the intricate spatial geometry. This ensures the generation of coherent and lifelike human figures. With the introduction of a comprehensive dataset, HumanVerse, and the implementation of a Latent Structural Diffusion Model in conjunction with a Structure-Guided Refiner, their framework sets a new standard in the creation of high-quality, realistic human images. It offers an unparalleled level of detail and layout diversity across various settings, establishing a new benchmark in the realm of AI-generated human imagery.