AI Image Evaluation: Find Out Whether Your Image Is Ready to Generate in Redraw

The quality of an AI-generated render does not depend only on the selected model or the instructions in the prompt. The reference image also directly affects how the AI interprets the project.
Issues such as an excessively tilted camera, floating objects, overlapping elements, insufficient lighting, or a cluttered scene may lead to unwanted changes in the result.
Redraw’s AI Image Evaluation analyzes your scene before generation, assigns it a score, and identifies areas that can be improved to increase the chances of achieving a good result.

What is Redraw’s AI Image Evaluation?

Image Evaluation is a Redraw feature that checks whether an uploaded image is properly prepared to be interpreted by a rendering AI.
During the analysis, the AI considers factors related to:

• camera position;
• scene composition;
• lighting;
• framing;
• object organization;
• object readability;
• technical quality of the 3D model.

At the end of the analysis, you receive an overall score, individual scores for each criterion, and explanations of the main issues found.
The feature does not edit or modify your project. It only analyzes the image and provides guidance before generation.

How does the evaluation work?

When you upload an image to a compatible Redraw tool, the platform analyzes it and displays its Image Score.
The evaluation screen may include:

• a final score from 0 to 10;
• the image’s overall rating;
• a summary of the AI analysis;
• individual scores for each criterion;
• explanations of the issues found;
• warnings about critical problems that limited the score;
• access to related Redraw Academy content.

The analysis takes place before generation. You can then choose whether to continue with the current image or correct the identified issues before using your coins.

What does the AI evaluate?

The feature considers different factors that may affect the AI’s ability to understand the project.

Camera angle

This criterion evaluates whether the camera position makes the environment and its proportions easy to understand.
Cameras that are too high, too low, too close, or excessively tilted can distort the perspective and make the scene harder to read.
A well-positioned camera should clearly show the space, preserve understandable proportions, and avoid excessive distortion.

Technical elements

This criterion identifies possible modeling or scene-construction issues, including:

• floating objects;
• furniture intersecting the floor or walls;
• overlapping elements;
• misaligned walls, floors, or roofs;
• incorrectly cropped objects;
• inaccurate connections between walls, ceilings, and furniture;
• elements placed at an incorrect scale;
• software interface elements visible in the image.

The AI may interpret these issues as part of the project and reproduce or modify them during generation.

Object readability

This criterion analyzes whether the objects in the scene are easy to identify.
Objects with low contrast, overlapping shapes, or colors that blend into the background may be interpreted incorrectly.
Clear contours, recognizable shapes, and visible separation between elements improve the chances that the AI will preserve the original structure.

Lighting

This criterion checks whether there is enough light to understand the environment, furniture, and structural elements.
Images that are too dark, overexposed, or filled with strong shadows may hide important details.
The image does not need to be photorealistic, but the entire scene should be visible and readable.

Framing

This criterion evaluates how the environment is positioned within the image.
Good framing shows the most important elements without unnecessary cropping, large empty areas, or hidden sections of the project.
Avoid cutting off furniture, walls, doors, windows, or structural elements that are important for understanding the scene.

Visual clutter

This criterion analyzes whether too much information is competing for the AI’s attention.
Interpretation may be affected by:

• too many decorative objects;
• vegetation covering parts of the project;
• several overlapping elements;
• overly busy backgrounds;
• guides, axes, selections, or software menus.

A cleaner scene makes it easier for the AI to distinguish the main structure from decorative details.

Scene density

This criterion evaluates the number and distribution of elements in the environment.
A scene that is too empty may not provide enough information about the project. A scene that is too crowded may make individual objects difficult to identify.
The ideal scene contains a balanced number of elements, with clear visual separation and understandable circulation areas.

How should I interpret the score?

The score indicates how well prepared the image is to be understood by the AI.
A high score means that the scene provides good conditions regarding camera position, composition, lighting, and organization. This increases the chances that the project structure will be interpreted correctly.
A low score indicates that certain issues may affect the result. In this case, review:

• the criteria with the lowest scores;
• warnings highlighted by the AI;
• issues classified as critical;
• the explanation provided in the evaluation.

The score is guidance, not a guarantee. The final generation also depends on the selected model, prompt, and settings.

Why can a critical issue limit the final score?

Not every issue has the same impact.
A major technical error may compromise the entire generation, even when the other criteria receive good scores.
For example, a floating tree, furniture intersecting the floor, or an incorrectly positioned structure may be reproduced or reinterpreted by the AI.
When a critical problem is detected, Redraw identifies the criterion that limited the score and provides an explanation. This should be the first issue you correct before generating.

Why did my image receive a low score?

Even a visually appealing image may contain issues that make it difficult for the AI to interpret, such as:

• unsuitable perspective;
• an excessively tilted camera;
• floating objects;
• modeling errors;
• poor framing;
• too many elements;
• difficult-to-read structures;
• lighting that hides important sections;
• important elements cropped at the edges;
• insufficient separation between objects and the background;
• visible software interface elements.

High resolution alone does not guarantee a high score. Scene organization, clarity, and technical quality are also considered.

How can I improve the image score?

Start by reviewing the criteria with the lowest scores and any issues marked as critical.
Common improvements include:

• positioning the camera at a more natural height;
• avoiding extremely high, low, or tilted perspectives;
• correcting floating or overlapping objects;
• fixing furniture that intersects floors or walls;
• removing menus, toolbars, axes, and selections;
• improving scene lighting;
• reducing unnecessary decorative elements;
• avoiding cuts in important furniture and structures;
• increasing visual separation between elements;
• correcting connections between walls, ceilings, and furniture;
• reviewing proportions and scale;
• exporting the image at a suitable size and aspect ratio.

After making the changes, upload the image again to receive a new evaluation.

How should I prepare an image for AI rendering?

1. Adjust the camera

Choose a position that presents the space clearly. Avoid extreme aerial views or excessively tilted perspectives, especially in interior scenes.

2. Review the model

Confirm that:

• all objects are correctly supported;
• furniture does not intersect floors or walls;
• structural elements are aligned;
• object scales are consistent;
• there are no incorrectly cropped or overlapping elements.

3. Organize the scene

Keep the main elements visible and reduce decorative objects that are not necessary to understand the project.

4. Check the lighting

Make sure there are no completely dark or excessively bright areas. Walls, floors, furniture, and openings should remain visible.

5. Clean the image before exporting

Hide menus, toolbars, axes, guides, selected objects, and other software interface elements.

Do I need to upload a finished render?

No.
The image can be:

• a 3D model;
• a pre-render;
• a scene without realistic materials;
• a project that is still being developed.

The most important requirement is that the scene is organized, readable, and technically correct.
A simple image may receive a good score when it has a well-positioned camera, sufficient lighting, proper framing, and no major technical errors.
The feature evaluates whether the image is ready for generation, not only whether it looks photorealistic.

Does Image Evaluation work with interior and exterior projects?

Yes. The feature can analyze both interior and exterior scenes.
For interior projects, it may consider furniture distribution, wall readability, lighting, framing, and room organization.
For exterior projects, it may also analyze:

• facades;
• roofs;
• vegetation;
• vehicles;
• terrain;
• perspective;
• object distribution.

When should I use Image Evaluation?

We recommend using the feature before generating, especially when:

• you are using a 3D model that has not yet been rendered;
• a previous generation significantly changed the project structure;
• the scene contains many objects;
• the camera uses an unusual angle;
• the image was prepared by someone else;
• you are unsure about the project’s technical quality;
• the project contains complex architectural elements;
• you want to avoid unnecessary attempts;
• you want to identify issues before using coins.

Why should I evaluate the image before generating?

A well-prepared input image helps the AI correctly identify:

• walls;
• floors;
• furniture;
• doors and windows;
• materials;
• proportions;
• structural elements;
• decorative objects.

Correcting issues before generation reduces the risk of receiving images with distorted structures, modified objects, or incorrectly interpreted elements.
The evaluation makes the workflow more predictable by allowing you to detect potential problems before processing.

Frequently asked questions

Does Image Evaluation modify my project?

No. The feature only analyzes the uploaded image and provides a score, explanations, and recommendations. No automatic changes are made.

Does a high score guarantee a perfect result?

No. A high score indicates that the image provides good conditions for AI interpretation. The final result also depends on the selected model, prompt, and settings.

Can I generate with a low score?

Yes. The evaluation provides guidance and does not prevent generation. However, correcting the identified issues may improve your chances of obtaining a better result.

Why did I receive good individual scores but a low final score?

This may happen when the AI detects a critical issue in one specific criterion. Some technical errors have enough impact to limit the overall score.

Does the image need to be photorealistic?

No. 3D models and pre-rendered images may also receive good scores, provided that they are clear, organized, and free from major technical issues.

Can the feature detect floating objects?

Yes. The visual analysis may identify objects that are not correctly touching the floor, elements intersecting surfaces, and other possible modeling issues.

Can I evaluate an image exported from SketchUp?

Yes. Images exported from 3D modeling software can be evaluated. Before exporting, hide menus, toolbars, axes, guides, and selected elements.

Do I need to correct every criterion before generating?

Not necessarily. Prioritize the lowest scores and any issues classified as critical. Small adjustments may significantly improve how the AI reads the scene.

Does Image Evaluation consume coins?

No. The analysis can be performed before generation without consuming coins.

Evaluate before you generate

AI Image Evaluation helps you determine whether your project is ready to be processed.
With an overall score, criterion-based analysis, and practical recommendations, you can correct the most important issues before generation and increase the chances of achieving a good result on the first attempt.

Updated on: 15/07/2026