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

cs.CVAug 19, 2026

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

Authors: Ruiqi Zhang, Hao Zhu, Wenhao Zhang, Qi Zhang, Junqi Shi, Ming Lu, Xun Cao, Zhan Ma

Organizations: School of Electronic Science and Engineering, Nanjing University, Nanjing, 210023, China · vivo Mobile Communication Co., Ltd., Xi’an, 710055, China

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.

Figures & tables

Fig. 1: Overview of ReX-Shot for single-image rephotography. Left: Multidimensional rephotography capabilities. ReX-Shot virtually recaptures the reference scene as target shots with controllable camera geometry and photographic effects, including viewpoint, focal length, color temperature, exposure, saturation, and contrast. Right: Quantitative performance comparison. ReX-Shot supports viewpoint, focal-length, and photographic-effect control while outperforming representative baselines from different method categories. All results in the radar chart are evaluated on the DL3DV-140 [ 1 ] test set. For each metric, we globally normalize the scores across the three tasks, and the numerical labels on the concentric grid rings shown in the Viewpoint section also apply to the same metric in the Focal Length and Photographic Effects sections. The thick solid, thin solid, dashed, and dotted lines denote ReX-Shot, Novel View Synthesis, Super Resolution, and Instruction-Guided Editing, respectively.

Fig. 2: Pipeline of ReX-Shot. Given a single reference image, ReX-Shot first estimates a per-pixel point cloud and camera parameters, and then synthesizes rephotographed images with controllable viewpoint, focal length, and photographic effects. The pipeline comprises two stages. 1. Dual-space geometry and camera grounding combines pixel-space projection, which renders the estimated point cloud under the target camera, with feature-space projection, which implicitly transforms VGGT [ 29 ] and DINOv2 [ 31 ] features into camera-aware tokens through PRoPE-based cross-attention [ 41 ] . 2. Generative rephotography leverages the detail prior of a one-step generative super-resolution model to recover details missing from sparse 3D resampling under the guidance of dual-space conditions, while a parameterized photographic-effect encoder injects continuous effect controls into the DiT blocks. Snowflakes and flames denote frozen and trainable modules, respectively, and $\oplus$ denotes concatenation.

Fig. 3: Qualitative comparison with novel-view-synthesis baselines under viewpoint and focal-length control. For each example, the first row shows results at the target viewpoint with the focal-length scale fixed at $1.0\times$ . The second and third rows keep the target viewpoint fixed and enlarge the focal length by $2.0\times$ and $3.0\times$ , respectively. The input reference and ground-truth target images are shown in the leftmost and rightmost columns, respectively.

Method	DL3DV			MipNeRF360			Tanks & Temples			Average
Method	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$
AnySplat	16.51	0.464	0.461	14.66	0.457	0.477	13.61	0.425	0.521	14.93	0.449	0.486
CameraCtrl	14.45	0.391	0.480	13.71	0.400	0.604	12.69	0.411	0.545	13.62	0.401	0.543
LVSM	18.87	0.526	0.383	15.48	0.498	0.636	15.62	0.514	0.511	16.66	0.513	0.510
ViewCrafter	15.84	0.417	0.424	16.13	0.443	0.453	14.18	0.433	0.487	15.38	0.431	0.455
PE-Field	16.34	0.429	0.427	15.95	0.443	0.486	14.31	0.433	0.485	15.53	0.435	0.466

TABLE I: Quantitative comparison under joint viewpoint and focal-length control.

Method	DL3DV			MipNeRF360			Tanks & Temples			Average
Method	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$
AnySplat	18.07	0.638	0.223	12.39	0.479	0.419	12.65	0.526	0.386	14.37	0.548	0.343
CameraCtrl	17.63	0.481	0.273	15.56	0.438	0.503	14.53	0.469	0.428	15.91	0.463	0.401
LVSM	19.82	0.564	0.210	15.74	0.459	0.523	15.86	0.510	0.388	17.14	0.511	0.374
ViewCrafter	19.94	0.570	0.205	17.37	0.462	0.380	15.30	0.470	0.397	17.54	0.501	0.327
PE-Field	19.55	0.510	0.236	18.59	0.491	0.344	16.49	0.495	0.348	18.21	0.499	0.309

TABLE II: Quantitative comparison under viewpoint-only control.

Method	DL3DV			MipNeRF360			Tanks & Temples			Average
Method	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$
AnySplat	20.21	0.676	0.398	25.39	0.797	0.285	20.90	0.695	0.365	22.17	0.723	0.349
CameraCtrl	15.65	0.430	0.409	15.20	0.426	0.488	14.50	0.448	0.433	15.12	0.435	0.443
LVSM	22.16	0.628	0.259	24.37	0.689	0.279	22.98	0.681	0.247	23.17	0.666	0.262
ViewCrafter	16.40	0.428	0.427	19.70	0.551	0.403	15.90	0.486	0.408	17.33	0.488	0.413
PE-Field	17.08	0.453	0.390	18.47	0.509	0.359	16.37	0.489	0.389	17.31	0.484	0.379

TABLE III: Quantitative comparison under focal-length-only control.

Fig. 4: Qualitative focal-length-only comparison between TSD-SR and ReX-Shot under continuous focal-length enlargement.

Metric	Effect	ICEdit	IP2P	ReX-Shot
PSNR $\uparrow$	Color Temp.	23.28	20.23	31.72
	Exposure	17.25	16.13	30.18
	Saturation	19.92	27.75	30.13
	Contrast	19.86	20.64	28.59
	Average	20.08	21.19	30.16
SSIM $\uparrow$	Color Temp.	0.909	0.781	0.913

TABLE IV: Quantitative comparison under photographic-effect control.

Fig. 5: Qualitative comparison with instruction-guided image editing baselines under color-temperature control.

Fig. 6: Qualitative comparison with instruction-guided image editing baselines under exposure control.

Fig. 7: Qualitative comparison with instruction-guided image editing baselines under saturation control.

Fig. 8: Qualitative comparison with instruction-guided image editing baselines under contrast control.

Fig. 9: Inference latency comparison between ReX-Shot and representative feed-forward and generative methods. When applicable, end-to-end latency is decomposed into reconstruction, rendering for camera-geometry control, and effect editing for photographic-effect control.

Method	Focal Length Only			Viewpoint Only			Joint Control
Method	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$
SD3-FT	25.42	0.764	0.141	20.82	0.590	0.216	20.69	0.566	0.230
TSD-SR-FT	29.64 (+4.22)	0.867 (+0.103)	0.061 (−0.080)	22.30 (+1.48)	0.658 (+0.068)	0.134 (−0.082)	21.85 (+1.16)	0.620 (+0.054)	0.153 (−0.077)
TSD-SR-FT+FP	30.00 (+0.36)	0.878 (+0.011)	0.058 (−0.003)	23.13 (+0.83)	0.703 (+0.045)	0.122 (−0.012)	23.05 (+1.20)	0.679 (+0.059)	0.137 (−0.016)

TABLE V: Quantitative comparison across SD3-FT, TSD-SR-FT, and TSD-SR-FT+FP. Parentheses report changes relative to the preceding row.

Fig. 10: Qualitative results under joint control of viewpoint, focal length, and color temperature.

Fig. 11: Qualitative comparison across SD3-FT, TSD-SR-FT, and TSD-SR-FT+FP. The top row shows the generated images, and the bottom row shows the corresponding error maps. All error maps are visualized using the same color scale, with warmer colors indicating larger errors.

Fig. 12: PCA visualization of the projected output features, with the first three principal components mapped to RGB. The two rows correspond to two target viewpoints, and the columns show focal-length scales of $1.0\times$ , $2.0\times$ , and $3.0\times$ .

Controllable Image Generation

Controllable image generation steers images using spatial, visual, semantic, stylistic, or physical constraints beyond ordinary text prompts.

See all 215 papers

FlashReFocus: Interactive Image Refocusing in Seconds
Oct 16, 2026Ching-Heng Cheng, Chia-Ming Lee, Ming-Ching Chang +3Neural Rendering Diffusion Models for Image Restoration
National Cheng Kung University · State University of New York at Albany · National Yang Ming Chiao Tung University
Reshoot-Anything: A Self-Supervised Model for In-the-Wild Video Reshooting
Apr 23, 2026Avinash Paliwal, Adithya Iyer, Shivin Yadav +2Camera-Controlled Video Generation Video Diffusion Models
Morphic Inc.
NavCrafter: Exploring 3D Scenes from a Single Image
Apr 3, 2026Hongbo Duan, Peiyu Zhuang, Yi Liu +5Novel View Synthesis Single-View 3D Reconstruction
Center for Artificial Intelligence and Robotics, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China · Peng Cheng Laboratory, 518108, China · School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen University, China +1

Oct 16, 2026J/K move · Enter open · S save

FlashReFocus: Interactive Image Refocusing in Seconds

Ching-Heng Cheng, Chia-Ming Lee, Ming-Ching Chang, Xin Li+2

National Cheng Kung University · State University of New York at Albany · National Yang Ming Chiao Tung University

Neural Rendering Diffusion Models for Image Restoration

Open paper AskPDF arXiv

Single-image refocusing changes the focal plane and depth of field of a photograph after capture. It is inherently interactive: users try many focal points and blur strengths on one image, so each edit should be fast and change only the blur. Recent generative methods, however, rerun large diffusion models for every edit. We observe that the two sub-problems differ in kind: recovering the all-in-focus image is ill-posed and needs a generative prior, whereas rendering blur is largely determined by depth and the focal plane. We therefore propose FlashReFocus, which runs diffusion once per photograph and renders every edit with a lightweight network. To address the ill-posed restoration, a one-step diffusion deblurrer with a decoder refiner recovers fine detail efficiently while balancing fidelity and perceptual quality. For precise control, the renderer is conditioned on an explicit defocus map and uses defocus-bias attention, which lets pixels at similar defocus share information without interference from pixels at other depths; in-focus content stays unchanged, and editing the map yields tilt-shift and two focal planes. To further test generalization to unseen cameras, we collect 3CReal, a three-camera benchmark of 89 pairs. FlashReFocus achieves the best perceptual fidelity for zero-shot bokeh rendering on EBB! and 3CReal, refocuses 148.3x faster than GenRefocus, and takes about half a second per further edit, 462x faster.

Abstract

Controllable Image Generation

Explore similar work

FlashReFocus: Interactive Image Refocusing in Seconds

Reshoot-Anything: A Self-Supervised Model for In-the-Wild Video Reshooting

NavCrafter: Exploring 3D Scenes from a Single Image