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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2410.18830 |
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| _version_ | 1866913779454836736 |
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| author | Zhang, Xiaoyu Zhou, Teng Zhang, Xinlong Wei, Jia Tang, Yongchuan |
| author_facet | Zhang, Xiaoyu Zhou, Teng Zhang, Xinlong Wei, Jia Tang, Yongchuan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_18830 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multi-Scale Diffusion: Enhancing Spatial Layout in High-Resolution Panoramic Image Generation Zhang, Xiaoyu Zhou, Teng Zhang, Xinlong Wei, Jia Tang, Yongchuan Computer Vision and Pattern Recognition 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. |
| title | Multi-Scale Diffusion: Enhancing Spatial Layout in High-Resolution Panoramic Image Generation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2410.18830 |