The Camera Inside the Editor: Reading the Implicit Camera of Image Editors with Painted Calibration Patterns
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Abstract (arXiv)
Instruction-based image editors insert objects, restyle scenes and render new viewpoints, but it is unknown which camera they assume when they paint into a photograph. Asked to cover the floor with a checkerboard, an editor paints projective structure from which classical vanishing-point geometry reads pitch, roll, focal length, yaw and, on renders, the principal point, without any training. Unlike a calibrator such as GeoCalib, which estimates the camera of an image, this isolates the camera under which the editor paints. On 120 rendered cameras with exact ground truth, Qwen-Image-Edit-2511 paints tile edges that meet their vanishing points within 0.26 degrees, and its implicit camera matches the true one to 0.8 degrees in pitch and 6% in focal length, more accurately than GeoCalib except in roll. Asked to draw the horizon or mark a vanishing point instead, the editor fails, so this knowledge is revealed by painting and not by the explicit tasks we tried. The implicit camera has two priors: roll is pulled towards level (slope 0.71), and telephoto perspective towards a default of about 30 mm, which roughly matches the camera the models paint without any scene. For Qwen, the priors do not grow when blur removes four fifths of the line evidence. They are stronger on real photographs, and on NYUv2 a shorter wording of the task removes the difference for roll. On photographs from a 24--240 mm zoom lens the painted perspective grows with only 0.62 of the lens's slope, while GeoCalib and MoGe-2 saturate at about 52 and 42 mm. FLUX.1 Kontext and LongCat-Image-Edit are pulled much harder. Finally, from a level camera a camera-control LoRA executes pose commands at only 50--70% of their strength, and a board painted into its output agrees with the camera it produced.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 30 of its 47 references that are in the map. First posted on arXiv 29 Sep 2026.
Builds on
30 of 47 references.
References · 30
- 2025Qwen-Image Technical Reportcited by 973
- 2025FLUX.1 Kontext: Flow Matching for In-Context Image Generation and Editing in Latent Spacecited by 960
- 2025MoGe-2: Accurate Monocular Geometry with Metric Scale and Sharp Detailscited by 250
- 2025OmniGen2: Towards Instruction-Aligned Multimodal Generationcited by 241
- 2026LTX-2: Efficient Joint Audio-Visual Foundation Modelcited by 213
- 2024Probing the 3D Awareness of Visual Foundation Modelscited by 175
- 2019Zoom To Learn, Learn To Zoomcited by 114
- 2022Perspective Fields for Single Image Camera Calibrationcited by 75
- 2023DiffusionLight: Light Probes for Free by Painting a Chrome Ballcited by 63
- 2023Shadows Don't Lie and Lines Can't Bend! Generative Models don't know Projective Geometry...for nowcited by 61
- 2023Generative Models: What Do They Know? Do They Know Things? Let's Find Out!cited by 50
- 2024GeoCalib: Learning Single-image Calibration with Geometric Optimizationcited by 50
18 more
- 2026Image Generators are Generalist Vision Learnerscited by 40
- 2023Beyond Surface Statistics: Scene Representations in a Latent Diffusion Modelcited by 29
- 2024Generative Photography: Scene-Consistent Camera Control for Realistic Text-to-Image Synthesiscited by 26
- 2020Single View Metrology in the Wildcited by 20
- 2025Thinking with Camera: A Unified Multimodal Model for Camera-Centric Understanding and Generationcited by 19
- 2025AnyCalib: On-Manifold Learning for Model-Agnostic Single-View Camera Calibrationcited by 17
- 2023Enhancing Diffusion Models with 3D Perspective Geometry Constraintscited by 16
- 2024DiffCalib: Reformulating Monocular Camera Calibration as Diffusion-Based Dense Incident Map Generationcited by 9
- 2025GCC: Generative Color Constancy via Diffusing a Color Checkercited by 8
- 2024Boost 3D Reconstruction using Diffusion-based Monocular Camera Calibrationcited by 7
- 2026SpatialEdit: Benchmarking Fine-Grained Image Spatial Editingcited by 7
- 2026Open-Source Image Editing Models Are Zero-Shot Vision Learnerscited by 3
- 2026Camera Control for Text-to-Image Generation via Learning Viewpoint Tokenscited by 2
- 2025ControlVP: Interactive Geometric Refinement of AI-Generated Images with Consistent Vanishing Pointscited by 1
- 2026GenScale: A Benchmark for Relative Object Scale in Image Generation and Editingcited by 1
- 2026CameraEditor: Camera-Controlled Image Editing via Video-Prior Sequential Modelingcited by 1
- 2026Are Image Generators Zero-Shot Perceivers? A Rigorous Evaluationcited by 1
- 2026Shedding Light: A Benchmark for Evaluating Lighting Understanding in Generative Image Modelscited by 1
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