Input: 4-8 room photos, room order, and one staging direction
Output: a staged photo set and a 15-25 second walkthrough
Aspect: 16:9 for listings; 9:16 for social
Build log
1. Group by room
Name photos by room and shooting direction. A visitor must keep the same left-to-right order as the real space.
2. Lock structure
Verticals, windows, doors, floor joints, built-ins, outlets, and views stay fixed. Only furniture, light, and small decor may be staged.
3. Prove one room
Make one staged room image before any video. If the room fails, a walkthrough will only multiply the mistake.
4. Check against floor plan
Play from entry to exit. Room order, door swings, appliance state, and view direction must still match the listing.
Complete execution record
Task boundary and source gate
Treat AI home listing generator as a deliverable, not a definition. The job receives 4-8 room photos, room order, and one staging direction and owes a staged photo set and a 15-25 second walkthrough in 16:9 for listings; 9:16 for social. Write the rejection reason first, then turn it into the first hard constraint.
Require the room name, shooting direction, window direction, flooring material, built-in list, and a note on what may be staged. Check vertical lines, blown windows, mirrors, and owner permissions. Do not widen rooms, erase wires, invent views, or promise amenities that are not in the listing. Photographic staging is not permission to redesign the building.
The imgmov route
Name room photos by room and shooting order before upload. In Canvas, keep the walk-through order and use generation to repair perspective, light, or an empty-space decision. Structural facts and room dimensions stay in listing copy, not in the prompt.
Public Airbnb and seller-console pages did not expose a stable article body during research. Do not treat a generated size as platform-approved. Confirm the current listing dashboard limits before export and keep the platform check in the QA note.
Lock the variables that cannot drift
Lock verticals, floor joints, door swings, window direction, ceiling height, fixed fixtures, appliance brands, and furniture that remains after checkout. A generated room may look styled, but it must still fit the floor plan.
Put the locked fields at the top of the prompt, not at the end: Group photos by room, lock verticals and fixtures, then make one staged room proof before extending to a walkthrough. Never change the floor plan or the view. Save that prompt with the reference, ratio, and model so the next run starts from a decision instead of a guess.
Step-by-step build path
1) Group by room: Name photos by room and shooting direction. A visitor must keep the same left-to-right order as the real space. Leave one checkable artifact from this step; do not start the next until it exists. 2) Lock structure: Verticals, windows, doors, floor joints, built-ins, outlets, and views stay fixed. Only furniture, light, and small decor may be staged. Leave one checkable artifact from this step; do not start the next until it exists. 3) Prove one room: Make one staged room image before any video. If the room fails, a walkthrough will only multiply the mistake. Leave one checkable artifact from this step; do not start the next until it exists. 4) Check against floor plan: Play from entry to exit. Room order, door swings, appliance state, and view direction must still match the listing. Leave one checkable artifact from this step; do not start the next until it exists.
Change one named variable per rerun: prompt, reference, camera, duration, model, ratio, or export crop. If two variables change together, a better output cannot be reused because nobody knows which fix worked.
Review gates and evidence
Play the sequence from entry to exit. Check whether a viewer can keep left/right order, identify the room, read appliance state, and still match the photo set to the floor plan.
Keep source, approved wording, rejected version, correction, credit cost, and final crop next to the asset. The record exists so the next reviewer can reproduce the decision without a chat thread.
Failure diagnosis and retry ladder
If a wall moves, use more real stills and shorten the camera path. If light invents sunset, return to the original exposure. If furniture changes, reduce the scope to one staged surface.
Image retries have a ladder. Start with Agnes Image 2.5 for the cheap proof. Use SDXL Inpainting at 3 credits when only a masked region is wrong. Use Seedream 5.0 Lite or Nano-Banana 2 at 5 credits for a more complex rebuild. GPT Image 2 starts at 2 credits for Standard 1K and rises to 80 by quality and resolution. Name the failure before changing rungs.
Versioning and handoff
Deliver listing, social, and vertical versions separately. Every cut repeats the same room order and the same owner-approved facts.
The folder is boring on purpose: source, approved reference, generation settings, caption file, platform cuts, QA screenshots, and one correction line. A reusable asset is boring in the right way.
Why this is not a generic answer
Editing is not detached from production. Object detection, masks, reference images, version history, Canvas dependencies, and the final generation set stay together. A standalone generator can invent a scene; it will not preserve the source asset, the rejected versions, and the reason the approved crop won.
Do not widen rooms, erase wires, invent views, or promise amenities that are not in the listing. Photographic staging is not permission to redesign the building. If the request only asks what ai home listing generator means, a search page is faster. This page is useful when someone must deliver a staged photo set and a 15-25 second walkthrough under real constraints.
Frequently asked questions
Can it widen a room?
No. That misrepresents the property. Fix perspective and light, not dimensions.
The workflow is specific about its stopping point: it starts with 4-8 room photos, room order, and one staging direction and stops at a staged photo set and a 15-25 second walkthrough. In imgmov, upload the source, lock it as a reference, set 16:9 for listings; 9:16 for social, run one proof, and save the passing settings. If a reviewer cannot tell which version is approved, the workflow has failed even when the media looks good.
What should stay real?
Walls, windows, doors, views, fixtures, and anything included in the sale or stay.
Image retries have a ladder. Start with Agnes Image 2.5 for the cheap proof. Use SDXL Inpainting at 3 credits when only a masked region is wrong. Use Seedream 5.0 Lite or Nano-Banana 2 at 5 credits for a more complex rebuild. GPT Image 2 starts at 2 credits for Standard 1K and rises to 80 by quality and resolution. Name the failure before changing rungs.
How do I stage an empty room?
Choose one furniture style and one light direction. Avoid adding a different room layout.
The check is not “does it look AI-nice?” It is: Play the sequence from entry to exit. Check whether a viewer can keep left/right order, identify the room, read appliance state, and still match the photo set to the floor plan. Then the source, approved copy, rejected version, correction, and final crop stay in the same handoff folder.
Which format should I deliver?
16:9 for the listing, 9:16 for social, and a captioned version if there is spoken narration.
If a wall moves, use more real stills and shorten the camera path. If light invents sunset, return to the original exposure. If furniture changes, reduce the scope to one staged surface. Change one named variable, keep the old version, and record the credit cost. If the same failure repeats, fix the reference or scope rather than asking the prompt for forgiveness.
Can it invent amenities?
No. If the amenity is not in the listing, it cannot appear as real.
Editing is not detached from production. Object detection, masks, reference images, version history, Canvas dependencies, and the final generation set stay together. A standalone generator can invent a scene; it will not preserve the source asset, the rejected versions, and the reason the approved crop won. The handoff is reusable only when the source, reference ID, prompt, model, cost, rejection reason, and approved cut are linked. That chain is what makes the next ai home listing generator task faster.