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arXiv · 2608.18593

ReX-Shot: Single-Image Rephotography via Geometry- and Camera-Grounded Generation

Ruiqi Zhang·Hao Zhu·Wenhao Zhang·Qi Zhang·Junqi Shi·Ming Lu·Xun Cao·Zhan Ma
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Abstract

Single-image rephotography aims to synthesize new shots of a scene from a single reference image with specified viewpoints, focal lengths, and photographic effects, which are intrinsically coupled in imaging. Existing methods typically treat these factors separately and struggle under joint control: novel-view synthesis may introduce geometric distortions under focal-length changes, while super-resolution and instruction-guided editing remain confined to 2D and cannot reliably extend detail restoration or appearance control to novel viewpoints. We attribute these limitations to imperfect single-image 3D reconstruction and the sampling limit of continuous focal-length enlargement. To reduce projection bias from geometric errors, we use implicitly transformed foundation-model features for robust target-view guidance. We further formulate focal-length enlargement as a geometry-guided super-resolution problem and exploit generative detail priors to recover details lost during sparse 3D resampling. Built on this 3D-aware generative backbone, we lift photographic-effect control from 2D filtering to 3D-aware appearance editing, preserving content consistency across viewpoints and focal lengths. These components form ReX-Shot, a geometry- and camera-grounded generative framework for single-image rephotography. To our knowledge, ReX-Shot is the first unified framework to jointly control viewpoint, focal length, and parameterized photographic effects from a single image. Experiments show that ReX-Shot outperforms representative baselines across all three controls while enabling near-real-time interactive rephotography.

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Ruiqi Zhang, Hao Zhu, Wenhao Zhang, Qi Zhang, Junqi Shi, Ming Lu, Xun Cao, Zhan Ma. 2026-08-19. ReX-Shot: Single-Image Rephotography via Geometry- and Camera-Grounded Generation. https://arxiv.org/abs/2608.18593

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