HyperHuman: Hyper-Realistic Human Generation with Latent Structural Diffusion (2310.08579v2)

Abstract: Despite significant advances in large-scale text-to-image models, achieving hyper-realistic human image generation remains a desirable yet unsolved task. Existing models like Stable Diffusion and DALL-E 2 tend to generate human images with incoherent parts or unnatural poses. To tackle these challenges, our key insight is that human image is inherently structural over multiple granularities, from the coarse-level body skeleton to fine-grained spatial geometry. Therefore, capturing such correlations between the explicit appearance and latent structure in one model is essential to generate coherent and natural human images. To this end, we propose a unified framework, HyperHuman, that generates in-the-wild human images of high realism and diverse layouts. Specifically, 1) we first build a large-scale human-centric dataset, named HumanVerse, which consists of 340M images with comprehensive annotations like human pose, depth, and surface normal. 2) Next, we propose a Latent Structural Diffusion Model that simultaneously denoises the depth and surface normal along with the synthesized RGB image. Our model enforces the joint learning of image appearance, spatial relationship, and geometry in a unified network, where each branch in the model complements to each other with both structural awareness and textural richness. 3) Finally, to further boost the visual quality, we propose a Structure-Guided Refiner to compose the predicted conditions for more detailed generation of higher resolution. Extensive experiments demonstrate that our framework yields the state-of-the-art performance, generating hyper-realistic human images under diverse scenarios. Project Page: https://snap-research.github.io/HyperHuman/

HyperHuman: Advancements in Composable Human Image Synthesis

The field of generative artificial intelligence continues to strive towards achieving hyper-realistic human image synthesis, yet existing frameworks often exhibit discrepancies such as incoherent anatomy or unnatural poses. The paper "HyperHuman: Hyper-Realistic Human Generation with Latent Structural Diffusion" provides a significant contribution by introducing the HyperHuman framework, which addresses these challenges through a unified approach for generating realistic human images considering various configurations and conditions presented in the prompts.

Fundamental Contributions

The paper outlines a robust solution for synthesizing human images with high realism by focusing on both coarse and fine-grained structures of human bodies. This is achieved through a threefold approach:

1. The HumanVerse Dataset: Central to the research is the development of HumanVerse, an extensive dataset comprising 340 million human-centric images. Each image includes detailed annotation data such as human poses, depth maps, and surface-normal maps, offering rich structural context. This dataset surpasses previous collections in terms of resolution, diversity, and the granularity of annotations, thereby establishing a comprehensive foundation for training generative models.
2. Latent Structural Diffusion Model: This novel model integrates structural cues within a diffusion framework, jointly modeling RGB image appearance while denoising depth and surface-normal information. This method highlights the importance of capturing spatial and geometrical relationships, improving upon the limitations of pre-existing diffusion models, such as Stable Diffusion and DALL·E 2, which often struggled with coherent human anatomical features under structured variations.
3. Structure-Guided Refiner: To enhance detail and resolution further, the model includes a refiner stage, which leverages previous structural predictions to refine human image synthesis at higher resolutions.

Experimental Results

An extensive series of experiments underscore the framework's performance, demonstrating state-of-the-art results by effectively balancing realism, diversity, and control within image generation tasks. Quantitative metrics such as Fréchet Inception Distance (FID) and CLIP-based semantic alignment showcase the superior quality and alignment of the images generated by HyperHuman compared to other contemporary models. Notably, the system also excels where control mechanisms are required, significantly improving pose accuracy (documented through Average Precision and Recall metrics in pose-estimation tasks) relative to existing approaches.

Implications and Future Directions

The HyperHuman framework sets a new benchmark for the synthesis of human images, particularly in domains requiring fine manipulations of pose and style, such as digital content creation, virtual reality applications, and personalized content generation. As technology advances, future work may leverage enhancements in the quality of structural annotations or exploit advancements in LLMs to further refine text-to-image translation capabilities. Furthermore, ethical considerations such as the implications of hyper-realistic synthetic humans in deepfake creation must be addressed to ensure technology serves positively transformative societal needs.

In conclusion, the HyperHuman model marks an important advance in the compositional capabilities of generative AI, particularly in its approach to integrating multi-level structural information seamlessly, which could eventually lead to broader applications and enhancements in computational creativity and human-centered AI applications.