Scenario Walkable Room

Scenario Walkable Room

Overview

One photo of a room becomes a place to walk into and touch: a complete 3D Gaussian splat world (walls behind the camera included) with the room's small objects rebuilt as real 3D models a visitor can pick up, throw, and hear land. Restyle the photo first and the same room comes in several looks. The route follows the open-source image-blaster pipeline, with every generation on Scenario through MCP:

1. Uncover: describe the photo literally and list its single movable objects with a box, the surface each stands on, a size estimate, and materials. Pick two or three per room.
2. Plate: one removal-only image edit that erases those objects and nothing else.
3. Cut-outs: one image edit per object on the original photo, drawing it alone on white.
4. World: a single-image world model on the plate.
5. Objects: image-to-3D on each cut-out, with PBR textures.
6. Sounds: a few short impact one-shots per object.
7. Assembly: level and scale the world from its own splats, fit the lens it assumed, derive a collision heightfield, put each object back where the photo had it, and show it all with a streaming splat renderer and a physics engine.

Steps 1 and 7 are the agent's own work on downloaded files; every generation in between is an MCP call. Connection, uploads, and the core loop: see the scenario skill. Edits: scenario-image or scenario-gemini-image. Worlds: scenario-3d-worlds. 3D models: scenario-3d. Sound: scenario-audio or scenario-sonilo. If a sibling skill named here is missing from your available skills, ask the user to install it (npx skills add scenario-labs/skills --skill <name>); unattended, proceed from tool schemas and flag the gap.

Quick reference

Discover each paid stage's member with recommend, passing the capability and the user's own words, and read next_step before taking a pick, per the scenario skill. recommend has no capability for worlds, so find the single-image world member with search as scenario-3d-worlds teaches. Never assert a generative model's id as a constant. Read every pick's model_schema_get and price the exact payload with model_run dry_run=true before running it.

Launch every batch with wait=false, then jobs_wait on the ids, re-called with pending_job_ids on timeout. A world takes several minutes; a timeout is not a failure and never justifies a second model_run.

Worked example: the user's living room, walkable, with things to throw

1. Photo. upload_asset the user's photo (per the scenario skill: file_size, PUT the parts, upload_asset_complete) and reuse the asset id. With no photo, recommend with capability="txt2img" for a photoreal interior: a doorway view at about chest height, level and centered, wide lens, a few small objects on tables and the sofa. For several looks, edit that photo once per style, opening the prompt with the fixed architecture (windows, door, floor plan, camera, lens) before the new finishes, furniture, and light.

2. Filing. Create the run's collection before the first generation (collection_create through the catalog write lane, arguments under parameters), then collection_add_assets each keeper as it lands.

3. Uncover. asset_display each photo, describe it literally, and write the object list. Choose two or three objects per room: separate, unoccluded, one or two materials, standing on the floor, a table, or a seat.

4. Plate. recommend with capability="img2img" and "remove objects from a room photo, keep everything else", model_schema_get for the reference-image field, dry_run, then run with the photo as the reference:

   remove the following from the image: the {object} on the {support}, {where}; the {object} {where}

   asset_display the plate next to the photo. Removal edits sometimes recolor an object instead of erasing it, or take its neighbors too (every cushion on a bench, a carved screen beside a lamp): re-run with a narrower list, or drop that object.

5. Cut-outs. One edit per object on the original photo, never the plate, with the isolation prompt in references/objects.md. Name anything resting on the object so it stays out.

6. World. Find the single-image world member per scenario-3d-worlds, model_schema_get, dry_run, then run each plate with wait=false, full-resolution splats, one fixed seed for every room, and a prompt describing the room as it is without the removed objects. Collect the ids with jobs_wait (re-called with pending_job_ids; a world takes several minutes), then asset_download each .spz.

7. Objects. recommend with capability="img23d" and "textured PBR prop from a product cut-out", read the face-count, texture, and PBR fields off model_schema_get, dry_run, then one job per cut-out with wait=false and jobs_wait on the ids. Each job returns several assets (the mesh with its textures and previews): take the one whose asset_get mimeType is model/gltf-binary, inspect it in the viewer (scenario-3d), then asset_download it.

8. Sounds. recommend with capability="txt2audio" and "short impact sound effects", then one clip per object:

   Four separate one-shot impacts, each followed by a full second of silence: {the object, its material} {knocked over / dropped} onto {the room's floor}, {the character of the sound}. Close-miked, dry room, no music, no voices.

9. Assemble. Calibrate each world and place its objects per references/calibration.md, then build the page per references/viewer.md. Simplify the GLBs for the web (about 15,000 to 20,000 triangles, 1K WebP textures, Meshopt) and load physics after the room is on screen.