Adobe is expanding its experimental Project Indigo camera app with AI features designed not just to edit photos, but to explain how they could be improved. The new Adobe Project Indigo AI photo critique tool can assess elements such as framing, lighting, color, and emotional impact, while additional features offer suggestions for reshooting and editing an existing image.
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| Credit: Adobe |
The most interesting part of the update, however, is not the ability to remove objects or apply artistic styles. It is the attempt to make AI function more like a photography coach—although whether automated criticism can genuinely improve a person's photography will depend on how useful and specific its recommendations prove to be.
What Happened: Project Indigo Gets AI Photography Tools
Adobe is adding a collection of generative AI features to Project Indigo, its experimental iOS camera app. The app already focused on more advanced camera controls, including pro-style settings, multiple capture modes, and multi-frame super-resolution.
The new tools are grouped under an AI playground area and are being introduced as experiments rather than guaranteed features for a future public release.
One of the headline additions is an AI-powered photo critique. The tool provides an assessment of a picture's framing, lighting, colors, and emotional impact. Rather than simply telling users that a photo is “good” or “bad,” the feature is intended to offer more detailed observations about the choices made in the image.
A separate tool provides capture and editing suggestions. It can recommend changes to the way a photo is reshot, including adjustments to framing, exposure, and objects visible in the scene. The idea is to give users practical guidance before they take the picture again.
Adobe is also adding AI tools for removing unwanted objects, generating artificial depth of field, and applying different visual styles.
According to the source material, the new features currently use Google's Gemini-based Nano Banana model, although Adobe is reportedly keeping open the possibility of using other models, including its own Firefly technology.
Why Adobe's AI Photo Critique Matters
Most AI photo editing tools are built around a simple question: what should be changed?
The user selects an object, types a prompt, or chooses an effect. The software then produces a result. That approach can be convenient, but it does little to explain why an image may not be working in the first place.
Project Indigo is taking a different approach with its critique and reshooting suggestions. The software is attempting to connect the final image with the decisions that produced it.
That distinction matters. Removing a distracting object can improve one photograph, but understanding why the object distracted from the subject could help a photographer improve future shots without relying on an AI tool every time.
This is the strongest idea behind Adobe's update, in my view. The most valuable AI photography tools may not be the ones that produce the most dramatic visual changes. They may be the ones that help users understand composition, light, and visual balance well enough to make better decisions themselves.
That is an editorial interpretation, not a confirmed claim about Adobe's intentions. But the design of the features suggests a more educational approach than simply asking an AI model to transform an image.
Project Indigo's Critique Goes Beyond Generic Camera Advice
AI-powered photography guidance is not entirely new. Smartphone camera software has already experimented with features that offer suggestions about composition and framing.
The difference with Project Indigo is the level of detail promised by the critique feature. Instead of providing only broad advice, the tool is designed to comment on several aspects of a photograph, including lighting, color, framing, and emotional impact.
The capture and edit suggestions also connect advice to practical changes. A user might be told to alter the composition, adjust exposure, or remove a distracting element before taking another photograph.
That could make the tool more useful for beginners who understand that a picture feels “off” but cannot identify the reason.
There is also an important limitation. An AI system can describe photographic conventions, but photography is not governed entirely by rules. A subject can be deliberately centered. Harsh lighting can be intentional. A distracting object can sometimes contribute to a photograph's story.
As a result, the best use of the critique feature may be as a second opinion rather than an automatic authority.
AI Object Removal Gets a More Practical Interface
Project Indigo is also expanding AI-powered object removal.
Many photo-editing tools already allow users to remove unwanted elements, but the process often requires manually circling or brushing over the object. That can be difficult when the target is small, irregularly shaped, or partially obscured.
Adobe's approach uses predefined categories for common distractions, including people in the background, trash, trash cans, wires, poles, fences, vehicles, and other clutter. Users can also describe a custom object they want removed.
The reported results include removing a person and an object they were holding from the background without obvious visual artifacts. That suggests the tool could be particularly useful for quick cleanup of travel, street, and everyday photographs.
The practical advantage here is not simply better AI generation. It is reducing the amount of precision required from the user. Instead of carefully selecting every unwanted object, a person can identify a category of distraction and let the software handle more of the work.
AI-Generated Depth of Field Could Change How Photos Are Edited
Another feature allows users to generate a depth-of-field effect after the photograph has been taken.
In traditional photography, background blur depends heavily on factors such as lens choice, aperture, distance, and sensor characteristics. Software can now simulate some of that effect after capture.
Project Indigo's tool gives users another way to separate a subject from its background without requiring the original image to have been captured with a strong optical blur.
The result can be useful, but it also highlights a broader challenge for computational photography: the more editing happens after capture, the more important accurate subject separation becomes. Errors around hair, hands, transparent objects, or complex backgrounds can still affect whether an artificial blur looks convincing.
The feature therefore belongs to a growing category of tools that make the original camera hardware less decisive for certain visual effects.
Style Transfer Is Familiar, but Still Part of the AI Playground
Adobe is also experimenting with style transfer. Users can apply looks inspired by watercolor, pen and ink, monochrome imagery, ink line with color wash, and backlit subjects.
The technology may produce polished results, but the concept itself is well established. Turning photographs into illustrations or stylized artwork has been available in consumer apps for years.
That makes style transfer less strategically interesting than the critique and reshooting features.
The more important question is whether Adobe can make AI editing feel controlled and intentional rather than encouraging users to endlessly generate variations. The app also includes a prompt-based editing option, despite the project's emphasis on more predictable button-driven tools.
That creates a tension within Project Indigo. Deterministic controls can make editing easier to understand, while open-ended prompts offer greater flexibility. The danger is that the latter can quickly turn photography into a process of generating increasingly disconnected images rather than improving the original photograph.
The Best AI Camera May Be the One That Teaches
The clearest value in Adobe's update is not another collection of generative effects. It is the possibility of making AI useful before the final edit.
That distinction could become increasingly important as image-generation tools make it easier to fix almost any photographic mistake after the fact. If every problem can simply be removed, replaced, or regenerated, users may have less reason to learn why the original image failed.
Project Indigo's critique tools point in the opposite direction. They could help users identify problems with composition and lighting before relying on generative editing.
The important caveat is that this only works if the feedback is specific, explainable, and flexible enough to recognize intentional creative choices. A tool that confidently applies generic photography rules to every image could become frustrating rather than educational.
In other words, Adobe's most promising experiment may be the one that does less to the photo itself. Teaching a user what to change could ultimately be more valuable than changing everything automatically.
What the Update Means for Photographers and Developers
For casual photographers, the new tools could make advanced editing more accessible. Removing clutter, simulating background blur, and receiving composition suggestions could reduce the technical knowledge needed to improve an image.
For more experienced photographers, the value may be different. AI critique could function as a quick second opinion, although professionals are unlikely to treat automated feedback as a replacement for personal judgment.
Developers and software companies are also watching a broader shift toward model-flexible AI applications. Project Indigo reportedly uses Google's Nano Banana for these features while leaving room to use other models, including Adobe's own Firefly technology.
That approach reflects a practical reality of AI software development: the model behind a feature may change, while the user experience remains the more durable part of the product.
Related Developments and What Could Happen Next
Project Indigo sits within a wider movement toward AI-powered camera assistance. Smartphone manufacturers and photo software companies are increasingly using AI not only to process images but also to advise users, remove unwanted elements, and reconstruct visual effects after capture.
Adobe's experiment is notable because it combines those capabilities with a more explicit attempt at photographic guidance.
The immediate next step is uncertain. The new tools are still being tested with select users, and Adobe has not guaranteed that every experimental feature will receive a broad release.
That makes the current update less about a finished product and more about testing which forms of AI assistance people actually find useful.
Adobe Project Indigo is adding AI photo critique, reshooting advice, object removal, artificial depth of field, and style transfer to its experimental iPhone camera app.
The individual features are not equally new. Object removal, background blur, and artistic filters are already familiar categories. The more interesting experiment is the attempt to make AI explain what could improve a photograph rather than simply alter the final result.
That could prove to be Project Indigo's most valuable direction. If Adobe can make automated feedback specific without treating photographic rules as absolute, AI could become more than an editing shortcut. It could become a tool that helps people understand the decisions behind better photographs.
