Generating Exquisite High-Resolution Human-Centric Scenes with Exceptional Text-Image Correspondence Using Pretrained Diffusion Models

Core Concepts

BeyondScene, a novel framework, overcomes the limitations of existing text-to-image diffusion models by generating exquisite higher-resolution (over 8K) human-centric scenes with exceptional text-image correspondence and naturalness.

Abstract

The paper introduces BeyondScene, a novel framework for generating high-resolution human-centric scenes with exceptional text-image correspondence and naturalness.
The key highlights are:

Detailed Base Image Generation: BeyondScene first generates a detailed base image focusing on crucial elements like human poses and detailed descriptions beyond the token limits of diffusion models. This enables precise control over the generated content.

Instance-Aware Hierarchical Enlargement: BeyondScene then progressively enlarges the base image while maintaining detail and quality. This is achieved through two novel techniques:

High frequency-injected forward diffusion: Adaptively injects high-frequency details into the upsampled image to enhance quality while preserving content.
Adaptive joint diffusion: Dynamically regulates the stride and conditioning of pose and text based on the presence of instances, enabling efficient and robust joint diffusion.

The paper demonstrates that BeyondScene significantly outperforms existing methods in terms of text-image correspondence, global and human naturalness, while generating images up to 8192×8192 resolution, surpassing the technical classification of 8K.

Stats

"Training image size is limited, text encoder capacity is restricted, and generating complex scenes with multiple humans is inherently difficult for existing text-to-image diffusion models."
"BeyondScene generates exquisite higher-resolution (over 8K) human-centric scenes with exceptional text-image correspondence and naturalness."

Quotes

"BeyondScene employs a staged and hierarchical approach to initially generate a detailed base image focusing on crucial elements in instance creation for multiple humans and detailed descriptions beyond token limit of diffusion model, and then to seamlessly convert the base image to a higher-resolution output, exceeding training image size and incorporating details aware of text and instances via our novel instance-aware hierarchical enlargement process."
"BeyondScene surpasses existing methods in terms of correspondence with detailed text descriptions and naturalness, paving the way for advanced applications in higher-resolution human-centric scene creation beyond the capacity of pretrained diffusion models without costly retraining."

Key Insights Distilled From

BeyondScene

by Gwanghyun Ki... at arxiv.org 04-09-2024

https://arxiv.org/pdf/2404.04544.pdf

Deeper Inquiries

How can the instance-aware hierarchical enlargement process be further improved to achieve even higher resolutions and better quality?

The instance-aware hierarchical enlargement process can be enhanced by incorporating more advanced techniques for upscaling and refining images. One approach could involve leveraging advanced super-resolution algorithms, such as generative adversarial networks (GANs) or progressive growing GANs, to enhance the resolution of the images further. Additionally, integrating more sophisticated inpainting methods to fill in missing details and improve the overall coherence of the scene could lead to better quality outputs. Furthermore, exploring novel ways to incorporate semantic information and context awareness into the enlargement process could help generate more realistic and detailed scenes at even higher resolutions.

What are the potential limitations of the proposed approach, and how can they be addressed in future work?

One potential limitation of the proposed approach could be the computational complexity and resource requirements, especially when dealing with ultra-high resolutions. This could lead to longer processing times and increased memory usage, making it challenging to scale the method to even higher resolutions. To address this, future work could focus on optimizing the algorithms and leveraging parallel processing techniques to improve efficiency and reduce computational overhead. Additionally, exploring novel architectures and model optimizations tailored for high-resolution image generation could help mitigate these limitations.

How can the techniques developed in BeyondScene be applied to other domains beyond human-centric scene generation, such as object-centric or landscape generation?

The techniques developed in BeyondScene can be adapted and applied to other domains beyond human-centric scene generation, such as object-centric or landscape generation. For object-centric generation, the instance-aware hierarchical enlargement process can be modified to focus on individual objects or entities within a scene, allowing for detailed control and refinement at a higher resolution. Similarly, for landscape generation, the approach can be extended to incorporate terrain features, vegetation, and environmental elements, enabling the creation of realistic and detailed landscapes at ultra-high resolutions. By tailoring the techniques to specific domains and adjusting the input data and conditioning factors accordingly, BeyondScene's methods can be effectively utilized for a wide range of image generation tasks.