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Multi-Scale Diffusion: Enhancing Spatial Layout in High-Resolution Panoramic Image Generation

Year: 2024

Type: preprint

Abstract: Diffusion models have recently gained recognition for generating diverse and high-quality content, especially in image synthesis. These models excel not only in creating fixed-size images but also in producing panoramic images. However, existing methods often struggle with spatial layout consistency when producing high-resolution panoramas due to the lack of guidance on the global image layout. This paper introduces the Multi-Scale Diffusion (MSD), an optimized framework that extends the panoramic image generation framework to multiple resolution levels. Our method leverages gradient descent techniques to incorporate structural information from low-resolution images into high-resolution outputs. Through comprehensive qualitative and quantitative evaluations against prior work, we demonstrate that our approach significantly improves the coherence of high-resolution panorama generation.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2410.18830

Authors: Zhang, Xiaoyu, Zhou, Teng, Zhang, Xinlong, Wei, Jia, Tang, Yongchuan

Language: English

Topic: Generative Adversarial Networks and Image Synthesis

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W4404307494

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