Sergio is a specialist in generative AI and technology, and leads the Redraw development team.
AI Image Evaluation: Find Out if Your Image Is Ready to Generate in Redraw
Redraw's AI Image Evaluation scores your scene from 0 to 10 before generation and shows what to fix to improve your render. Learn the criteria it analyzes.
4 min
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06.05.2026
Sergio is a specialist in generative AI and technology, and leads the Redraw development team.
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You upload an image to generate a render, but the result doesn't come out as expected. Very often the problem is not the AI model or the prompt, but the input image itself.
A camera tilted too far, objects that are hard to identify, floating elements, too much information, or modeling flaws can all make the scene harder to interpret. Even a visually beautiful image can contain issues that reduce the quality of the generation.
Redraw's AI Image Evaluation analyzes the project before generation, assigns a score, and shows which aspects can be improved to increase the chances of a good result.
What is Redraw's AI Image Evaluation?
Image Evaluation is a tool that checks whether the uploaded image is well prepared to be interpreted by a rendering AI.
The AI visually analyzes the scene, considering factors related to the camera, composition, lighting, object organization, and the technical quality of the project.
At the end of the analysis, the user receives an overall score, individual grades per criterion, and an explanation of the main points identified.
How does Image Evaluation work in practice?
When you upload an image to a compatible Redraw tool, the platform analyzes the file and presents the Image Score.
The screen shows:
- a final score from 0 to 10;
- an overall classification of the image;
- a summary of the AI's analysis;
- an individual grade for each criterion;
- explanations of the problems identified;
- an indication of serious problems that limited the score;
- access to Academy content with complementary guidance.
The analysis happens before generation. That way, you can decide whether to continue or fix the image first.
What does the AI evaluate in the image?
Image Evaluation considers factors that can directly influence the AI's ability to understand the project.
The main criteria include:
- camera angle;
- technical elements;
- object legibility;
- lighting;
- framing;
- visual clutter;
- scene density.
The AI looks at the image as a whole, considering not only its general appearance but also the clarity of the composition, the structure of the space, and how the elements relate to each other.
What does each evaluation criterion mean?
Camera angle
Evaluates whether the camera position allows the space to be understood correctly.
Cameras that are too high, tilted, crooked, or too close to elements can distort perspective and make the space harder to read.
A well-positioned camera presents the space clearly, with proportions that are easy to understand and no excessive deformation.
Technical elements
Identifies possible modeling flaws or problems in how the scene was built.
This can include:
- floating objects;
- furniture clipping through the floor;
- overlapping elements;
- misaligned walls or roofs;
- objects cut off incorrectly;
- imprecise joints between walls, ceiling, and furniture;
- elements placed out of scale;
- parts of the software interface still visible in the image.
These problems can directly affect the result, because the AI may interpret the flaw as part of the project.
Object legibility
Analyzes whether the components of the scene are clearly identifiable.
Objects with low contrast, overlapping, or blending into the background can be interpreted incorrectly during generation.
The clearer the contours, shapes, and boundaries between elements, the better the chance the AI will correctly preserve the project's structure.
Lighting
Checks whether the scene has enough light for the elements to be understood.
Images that are too dark, blown out, or full of very harsh shadows can hide important details.
The lighting doesn't need to be photorealistic, but it must allow a complete reading of the space, the furniture, and the structures.
Framing
Evaluates how the space is positioned within the image.
Good framing presents the main elements without unnecessary crops, without large empty areas, and without hiding important parts of the scene.
Furniture, walls, openings, and central structures should not be partially out of frame when they matter for reading the project.
Visual clutter
Analyzes whether there is too much information competing for attention.
Too many decorative objects, vegetation covering furniture, overlapping elements, or very busy backgrounds can make the scene harder to interpret.
A cleaner image helps the AI identify what belongs to the main structure and what is just a decorative element.
Scene density
Evaluates the quantity and distribution of elements in the space.
A scene that is too empty may not offer enough information about the project. On the other hand, an overloaded scene can make it hard to identify each object.
The ideal is a balanced amount of elements, with good visual separation and understandable circulation areas.
Why can an image get a low score?
An image can look good at first glance and still contain problems that get in the way of generation, such as:
- inadequate perspective;
- floating objects;
- modeling flaws;
- poor framing;
- too many elements;
- furniture clipping through surfaces;
- structures that are hard to understand;
- lighting that hides important parts;
- important elements cut off at the edges;
- little visual separation between objects and background.
The score is meant to show how prepared the image is to be interpreted by the AI.
Good resolution alone does not guarantee a good evaluation. The organization of the scene and the clarity of the elements also directly influence the result.
Why can a serious problem limit the final score?
Not all problems have the same impact.
A significant technical error can compromise the entire generation, even when other criteria have good grades.
For example, a floating tree, a piece of furniture clipping through the floor, or a badly positioned structure can be reproduced or reinterpreted by the AI in the final result.
When this happens, Redraw tells you which criterion limited the score and shows an explanation on screen.
This helps you identify the most important thing to fix before generating.
How to improve your image score?
Before starting a generation, review the criteria with the lowest grades and fix the problems indicated.
Common improvements include:
- positioning the camera at a more natural height;
- avoiding very tilted perspectives;
- fixing floating or overlapping objects;
- removing technical elements from the software;
- improving the scene's lighting;
- reducing unnecessary decorative objects;
- avoiding crops on important furniture and structures;
- keeping elements more separated and identifiable;
- fixing flaws where walls, ceiling, and furniture meet;
- reviewing proportions and scale;
- using an image with adequate size and aspect ratio.
After the changes, the image can be submitted again for a new evaluation.
Do I need to upload a finished render to get a good score?
No. The image can be a 3D model, a pre-render, or a project without realistic finishing yet.
What matters most is that the scene is organized, legible, and technically correct.
A simple image can get a good evaluation when it has:
- a well-positioned camera;
- clear objects;
- balanced composition;
- sufficient lighting;
- no modeling flaws;
- adequate framing;
- good separation between elements.
The tool evaluates whether the image is ready for generation, not just whether it looks realistic.
Does Image Evaluation work for interior and exterior projects?
Yes. The tool can analyze both interior and exterior scenes.
In interior projects, the evaluation considers points like furniture distribution, wall legibility, lighting, framing, and the organization of the space.
In exterior projects, it can also identify problems related to vegetation, vehicles, facades, roofs, perspective, and the distribution of elements in the scene.
When should you use Image Evaluation?
Use Image Evaluation before generating whenever you want to check that the project gives the AI good conditions to understand the scene.
It is especially useful when:
- you are using a 3D model that hasn't been rendered yet;
- the previous result changed the project's structure too much;
- there are many objects in the space;
- the camera is at an unusual angle;
- you received an image prepared by someone else;
- there are doubts about the quality of the scene;
- you want to avoid unnecessary attempts;
- the project has complex technical elements;
- you want to identify problems before spending coins.
Why evaluate the image before generating?
A well-prepared input image helps the AI correctly recognize walls, furniture, openings, materials, proportions, and structural elements.
By fixing problems before generation, you reduce the risk of getting results with distorted structures, modified objects, or elements interpreted incorrectly.
The evaluation also makes the process more predictable. Instead of discovering problems only after generating, you can identify them beforehand and adjust the project.
How to prepare an image for AI rendering?
To improve how the scene reads, start with the camera. Use a position that shows the space clearly and avoid angles that are too high, too low, or too tilted.
Next, review the modeling. Check that objects are properly resting on the floor, that nothing is clipping through surfaces, and that walls, ceilings, and furniture are well aligned.
It's also important to keep the scene organized. Avoid an excess of decorative objects and keep the main elements visible.
Finally, export the image without menus, bars, axes, selections, or any other element of the software interface.
Tips for getting a good evaluation
Avoid very steep aerial angles, especially in interior scenes.
Check that all objects are properly resting on the floor and that nothing is clipping through walls, furniture, or other surfaces.
Use enough lighting to reveal the entire space, without areas that are too dark or too bright.
Don't cut off important furniture or structures at the edges of the image, and keep good visual separation between walls, floor, ceiling, and furniture.
Hide guide lines, selections, axes, and other software elements before exporting the image.
Frequently asked questions
What is Redraw's AI Image Evaluation?
It is a tool that analyzes whether an image is well prepared to be used in an AI generation. It assigns a score and shows which aspects of the scene can be improved.
Does Image Evaluation generate or modify my project?
No. The tool only analyzes the uploaded image and presents a score, explanations, and recommendations. No changes are made automatically.
Does a high score guarantee the generation will be perfect?
No. The score indicates that the image offers good conditions to be interpreted by the AI, but the result can also vary depending on the model, the prompt, and the settings used.
Can I generate even with a low score?
Yes. The evaluation works as guidance. You can continue with the generation, but fixing the points indicated can increase your chances of a better result.
Why did my image get good grades on some criteria but a low final score?
This can happen when the AI identifies a serious problem in a specific criterion, especially in technical elements. Some errors have enough impact to compromise the generation and can therefore limit the final score.
Does the image need to be photorealistic?
No. 3D models and pre-rendered images can also get good grades. The scene needs to be clear, organized, and free of significant technical errors.
Does the evaluation detect floating objects?
Yes. The visual analysis can identify objects without proper contact with the floor, elements clipping through surfaces, and other possible modeling flaws.
Does the tool evaluate interior and exterior images?
Yes. Image Evaluation can be applied to both interior and exterior scenes.
Do I need to fix every criterion before generating?
Not necessarily. Prioritize the criteria with the lowest grades and the problems flagged as serious. Small adjustments can significantly improve how the AI reads the image.
Can I evaluate an image made in SketchUp?
Yes. Images exported from 3D models can be evaluated, as long as they show only the scene, without menus, bars, or other elements of the software interface.
Does Image Evaluation consume coins?
No. The evaluation works as an analysis step before generation and is free of charge.
Evaluate before you generate
AI Image Evaluation helps you understand whether your project is truly ready for a generation.
With a clear score, per-criterion analysis, and practical guidance, it becomes much easier to fix problems before processing starts.
The result is a safer workflow, with fewer unnecessary attempts and a better chance of getting a great image from the very first generation.
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