What AI room redesign tools can and can't do (notes for people building or evaluating them)

If you are building a tool that takes a photo of a room and returns a redesigned version, or you are just evaluating one, this is a summary of where the category tends to work and where it tends to break.

The core trick: change surfaces, keep structure

A plain image generator invents a new room. A useful room redesign tool has to do the opposite: keep the walls, windows, doors and perspective from the photo and change only what the user asked about. Most of the techniques here are variations of conditioning a diffusion model on structure, for example edges or depth estimated from the input photo. ControlNet-style conditioning is the best-known example. The effect for the user is that the window stays in the same place while the wall colour or the sofa changes.

If you want a quick feel for how this category behaves, try one. We build HausDraft's AI room design tool, where your first image is free, no card needed. Whichever tool you evaluate, give it your worst, darkest room photo first, because that tells you far more than a staged example does.

Better support for floor plans and measurements next to the render, so that "I like this look" and "this fits in my room" are answered by the same tool. If you have built something in this area, I would be glad to hear what broke for you in the comments.