Quantum computing is often lauded for its potential to revolutionize problem solving, especially when classical computers face substantial limitations. While much of the discussion has revolved around the theoretical advantages of asymptotic scaling, it is crucial to identify practical applications for quantum computers in finite-size problems. Concrete examples demonstrate what problems quantum computers can address more efficiently than their classical counterparts and how quantum algorithms can be employed for these tasks. In recent years, collaborative research efforts have explored real-world applications for quantum computing, offering insights into specific problem domains that can 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 to controllable human generation have limitations. The researchers proposed that 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 generating hyper-realistic human images.
Generating hyper-realistic human images from user conditions such as text and pose is crucial for applications such as image animation and virtual testing. Early methods using VAE or GAN faced limitations in stability and training capacity. Diffusion models have revolutionized generative ai, but existing T2I models struggled with coherent human anatomy and natural poses. HyperHuman presents 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, with 340 million annotated images. HyperHuman incorporates a latent structural diffusion model that removes depth and surface normal noise while generating RGB images. A structure-guided refiner improves the quality and detail of synthesized images. Its framework produces hyper-realistic human images in various scenarios.
Their study evaluates the HyperHuman framework using several 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 balanced performance between 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. Develop high-quality, diverse, text-aligned images by leveraging the HumanVerse dataset and a latent structural diffusion model. The frame structure-guided refiner improves visual quality and resolution. It significantly advances the generation of hyper-realistic human images with superior performance and robustness compared to previous models. Future research can explore the use of deep backgrounds such as LLMs to achieve text-to-pose generation, eliminating the need to input the body skeleton.
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Hello, my name is Adnan Hassan. I’m a consulting intern at Marktechpost and soon to be a management trainee at American Express. I am currently pursuing a double degree from the Indian Institute of technology, Kharagpur. I am passionate about technology and I want to create new products that make a difference.
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