New Method for Generating Hyper-Realistic Human Images: The HyperHuman Framework and its Text-to-Image Model

Quantum computing is often hailed for its revolutionary problem-solving capabilities, especially in situations where classical computers face significant limitations. While most discussions revolve around the theoretical advantages of asymptotic performance, it is crucial to identify the practical applications of quantum computing in finite-scale problems. Specific examples demonstrate how quantum computers can solve certain problems more efficiently than classical computers and how quantum algorithms can be used to accomplish these tasks. In recent years, extensive research efforts have explored the real-world applications of quantum computing, providing insights into the benefits of this emerging technology for specific problem domains.

The diffusion-based Text-to-Image (T2I) models have become the preferred approach for image generation due to their scalability and training stability. However, models like Stable Diffusion require assistance to generate high-fidelity human images. Traditional controlled human generation methods have limitations. Researchers have proposed the HyperHuman framework to overcome these challenges, which captures the correlation between appearance and latent structure. It includes a large-scale human-centric dataset, a latent structure diffusion model, and a structure-guided refiner, achieving state-of-the-art performance in generating hyper-realistic human images.

Generating hyper-realistic human images from user conditions such as text and poses is crucial for applications like image animation and virtual try-on. Early methods using VAEs or GANs had limitations in training stability and capacity. Diffusion models have revolutionized generative AI, but existing T2I models exhibit inconsistencies in human anatomy and natural poses. HyperHuman introduces a framework that captures appearance-structure correlations, ensuring high realism and diversity in human image generation while addressing these challenges.

HyperHuman is a framework for generating hyper-realistic human images. It includes a massive human-centric dataset called HumanVerse, which contains 340 million images annotated with comprehensive information such as human poses, depth, and surface normals. HyperHuman employs a latent structure diffusion model that denoises depth and surface normals while generating RGB images simultaneously. Their framework produces hyper-realistic human images in various scenarios.

Their research evaluates the HyperHuman framework using various metrics, including FID, KID, and FID CLIP for measuring image quality and diversity, CLIP similarity for text-image alignment, and pose accuracy metrics. HyperHuman performs excellently in image quality and pose accuracy, ranking second in CLIP scores despite using a smaller model. Their framework demonstrates balanced performance in image quality, text alignment, and common CFG scales.

In conclusion, the HyperHuman framework presents a novel approach to generating hyper-realistic human images, overcoming challenges in consistency and naturalness. It leverages the HumanVerse dataset and a latent structure diffusion model to generate high-quality, diverse, and text-aligned images. The structure-guided refiner of the framework enhances visual quality and resolution. It has made significant progress in generating hyper-realistic human images, exhibiting higher performance and robustness compared to previous models. Future research can explore the use of deep priors, such as LLMs, to achieve text-to-pose generation and eliminate the need for human skeleton input.