For numerous applications, such as graphic video production and digital try-ons, the emergence of hyper-realistic human images under user-defined conditions like pose and context is highly significant. While early methods focused on enhancing realism through generative adversarial networks (GANs) or using variational auto-encoders (VAE) in a reconstructive manner, limitations persisted, particularly in scalability and diversity due to training fragility and model capacity constraints.
The advent of diffusion models (DMs) has ushered in a new era of realistic synthesis, surpassing traditional Generative AI architectures. However, generating coherent human images with natural poses and body structures remains challenging for text-to-image (T2I) models like Stable Diffusion and DALLE2. The intricate non-rigid deformations of the human body pose a significant hurdle, requiring nuanced details that are difficult to articulate through text alone.
Recent advancements, such as ControlNet and T2I-Adapter, have introduced a teachable unit to enhance image generation capabilities by integrating pre-trained DMs like Stable Diffusion. Despite these efforts, discrepancies in feature representation between control signals (e.g., pose maps) and generated images persist. In response, HumanSD proposes incorporating body bone information directly into the diffusion U-Net through channel-wise concatenation. However, this method is limited in generating visually diverse images and focuses solely on pose control, overlooking crucial structural aspects like depth and surface-normal maps.
The research outlined in this document introduces a novel framework named HyperHuman, aimed at producing diverse and highly realistic in-the-wild human images. It emphasizes the fundamental nature of human images across various levels of granularity, from body skeletons to detailed geometric features. The creation of HumanVerse, a comprehensive human-centric database with 340 million in-the-wild images and detailed annotations, serves as the foundation for modules like the Hidden Structural Diffusion Model and Structure-Guided Producer, designed to facilitate stable and realistic graphic generation.
By integrating geometry modeling, spatial relationships, and image appearance within a unified network architecture, HyperHuman ensures coherence and consistency across different aspects. The improved training schedule eliminates low-frequency information leakage, enhancing depth and surface-normal values in specific regions. The Structure-Guided Refiner refines predicted conditions for generating high-resolution images, while a robust fitness plan mitigates error accumulation in the generation pipeline.
HyperHuman represents a significant advancement in AI frameworks for generating diverse and realistic human images. For further details, please refer to the links provided below.
