Can We Generate Hyper-Realistic Human Images? This AI Paper Presents H …

Quantum computing is often heralded for its potential to revolutionize problem-solving, especially when classical computers face substantial limitations. While much of the discussion has revolved around theoretical advantages in asymptotic scaling, it is crucial to identify practical applications for quantum computers in finite-sized problems. Concrete examples demonstrate which problems quantum computers can tackle more efficiently than classical counterparts and how quantum algorithms can be employed for these tasks. Over recent years, collaborative research efforts have explored real-world applications for quantum computing, offering insights into specific problem domains that stand to benefit from this emerging technology.

Diffusion-based text-to-image (T2I) models have become a leading choice for image generation due to their scalability and training stability. However, models like Stable Diffusion need help creating high-fidelity human images. Traditional approaches for controllable human generation have limitations. Researchers proposed the HyperHuman framework overcomes these challenges by capturing correlations between appearance and latent structure. It incorporates a large human-centric dataset, a Latent Structural Diffusion Model, and a Structure-Guided Refiner, achieving state-of-the-art performance in hyper-realistic human image generation.

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

HyperHuman is a framework for generating hyper-realistic human images. It includes a vast human-centric dataset, HumanVerse, featuring 340M annotated images. HyperHuman incorporates a Latent Structural Diffusion Model that denoises depth and surface-normal while generating RGB images. A Structure-Guided Refiner enhances the quality and detail of the synthesised images. Their framework produces hyper-realistic human images across various scenarios.

Their study assesses the HyperHuman framework using various metrics, including FID, KID, and FID CLIP for image quality and diversity, CLIP similarity for text-image alignment, and pose accuracy metrics. HyperHuman excels in image quality and pose accuracy, ranking second in CLIP scores despite using a smaller model. Their framework demonstrates a balanced performance across image quality, text alignment, and commonly used CFG scales.

In conclusion, the HyperHuman framework introduces a new approach to generating hyper-realistic human images, overcoming challenges in coherence and naturalness. It develops high-quality, diverse, and text-aligned images by leveraging the HumanVerse dataset and a Latent Structural Diffusion Model. The framework’s Structure-Guided Refiner enhances visual quality and resolution. It significantly advances hyper-realistic human image generation with superior performance and robustness compared to previous models. Future research can explore the use of deep priors like LLMs to achieve text-to-pose generation, eliminating the need for body skeleton input.

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The post Can We Generate Hyper-Realistic Human Images? This AI Paper Presents HyperHuman: A Leap Forward in Text-to-Image Models appeared first on MarkTechPost.