Region Edit Pro

Inspiration

Precise image editing breaks down when multiple similar objects appear close together. Traditional region selection and prompts often fail to clearly express which object should be edited. We wanted to build a system that understands user intent as clearly as a human editor would.

What it does

Region Edit Pro enables semantic-guided, region-specific image editing. Users select one or more regions on an image and describe their desired edits. The system disambiguates visually similar targets using AI-driven visual understanding, then applies edits only to the intended region with high precision.

How we built it

We designed a two-stage AI pipeline:

Region Understanding (Gemini 3 Pro)

The selected region is highlighted within the full image context.

Gemini analyzes the highlighted area and user request.

It identifies the exact target, differentiates it from nearby similar objects, and generates a refined, unambiguous prompt.

Region Editing (nano banana pro)

The original image, region mask, and refined prompt are passed to the generation model.

Edits are strictly constrained to the masked region and semantically defined target.

All edited regions are composited into the final image.

Challenges we ran into

Ambiguous targets when objects look nearly identical.

Model overreach, where edits leaked into nearby regions.

Balancing visual context with strict spatial constraints.

Ensuring minimal UI while supporting complex multi-region workflows.

Accomplishments that we're proud of

Achieved significantly improved localization accuracy, even for adjacent similar objects.

Built a clean separation between visual understanding and image generation.

Designed a scalable architecture that supports masks, multiple regions, and future semantic extensions.

Maintained a minimal, focused user experience without extra guidance or friction.

What we learned

Localization failures are often semantic, not spatial.

Asking a model to understand first leads to dramatically better generation results.

Explicit disambiguation (“not this, only that”) is critical for reliable AI editing.

Multi-model collaboration outperforms single-model approaches for complex tasks.

What's next for Region Edit Pro

Support for free-form and brush-based masks

Automatic object-aware region suggestions

Parallel multi-region editing optimization

Expanding semantic control (e.g. “the most prominent object”, “closest to camera”)

Real-time refinement and preview loops

Built With

• gemini-3-pro
• nano-banana-pro
• react
• typescript