多尺度扩散模型提升全景图生成的空间一致性。
Multi-Scale Diffusion: Enhancing Spatial Layout in High-Resolution Panoramic Image Generation
- 分多分辨率层级生成，用低分辨率结构引导高分辨率输出
- 在高分辨率全景图生成中显著提升空间布局连贯性
- 适合需要高质量全景图像生成的研究与应用
扩散模型近年来在图像合成领域受到关注，能生成多样且高质量的内容，尤其适用于固定尺寸图像和全景图像生成。然而，现有方法在生成高分辨率全景图时，常因缺乏全局布局引导而出现空间布局不一致问题。本文提出多尺度扩散（MSD）框架，将全景图像生成扩展至多个分辨率层级。该方法利用梯度下降技术，将低分辨率图像的结构信息融入高分辨率输出。通过与先前方法的定性和定量对比评估，结果表明本方法显著提升了高分辨率全景图生成的连贯性。
原文摘要 · Abstract (English)
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.
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