UltraZoom: Generating Gigapixel Images from Regular Photos
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866916866556952576 |
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| author | Ma, Jingwei Jayaram, Vivek Curless, Brian Kemelmacher-Shlizerman, Ira Seitz, Steven M. |
| author_facet | Ma, Jingwei Jayaram, Vivek Curless, Brian Kemelmacher-Shlizerman, Ira Seitz, Steven M. |
| contents | We present UltraZoom, a system for generating gigapixel-resolution images of objects from casually captured inputs, such as handheld phone photos. Given a full-shot image (global, low-detail) and one or more close-ups (local, high-detail), UltraZoom upscales the full image to match the fine detail and scale of the close-up examples. To achieve this, we construct a per-instance paired dataset from the close-ups and adapt a pretrained generative model to learn object-specific low-to-high resolution mappings. At inference, we apply the model in a sliding window fashion over the full image. Constructing these pairs is non-trivial: it requires registering the close-ups within the full image for scale estimation and degradation alignment. We introduce a simple, robust method for getting registration on arbitrary materials in casual, in-the-wild captures. Together, these components form a system that enables seamless pan and zoom across the entire object, producing consistent, photorealistic gigapixel imagery from minimal input. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_13756 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | UltraZoom: Generating Gigapixel Images from Regular Photos Ma, Jingwei Jayaram, Vivek Curless, Brian Kemelmacher-Shlizerman, Ira Seitz, Steven M. Computer Vision and Pattern Recognition We present UltraZoom, a system for generating gigapixel-resolution images of objects from casually captured inputs, such as handheld phone photos. Given a full-shot image (global, low-detail) and one or more close-ups (local, high-detail), UltraZoom upscales the full image to match the fine detail and scale of the close-up examples. To achieve this, we construct a per-instance paired dataset from the close-ups and adapt a pretrained generative model to learn object-specific low-to-high resolution mappings. At inference, we apply the model in a sliding window fashion over the full image. Constructing these pairs is non-trivial: it requires registering the close-ups within the full image for scale estimation and degradation alignment. We introduce a simple, robust method for getting registration on arbitrary materials in casual, in-the-wild captures. Together, these components form a system that enables seamless pan and zoom across the entire object, producing consistent, photorealistic gigapixel imagery from minimal input. |
| title | UltraZoom: Generating Gigapixel Images from Regular Photos |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.13756 |