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arXiv · 2506.13756

UltraZoom: Generating Gigapixel Images from Regular Photos

Jingwei Ma·Vivek Jayaram·Brian Curless·Ira Kemelmacher-Shlizerman·Steven M. Seitz
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Abstract

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.

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Jingwei Ma, Vivek Jayaram, Brian Curless, Ira Kemelmacher-Shlizerman, Steven M. Seitz. 2025-06-16. UltraZoom: Generating Gigapixel Images from Regular Photos. https://arxiv.org/abs/2506.13756

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