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scenario-walkable-room

Use when one interior photo, uploaded or generated on Scenario, should become a room a visitor walks into in a web browser and plays with: props lifted out of the photo and rebuilt as 3D objects to grab, throw, and hear land, inside a Gaussian splat world, or restyled variants of one room. Keywords: image-blaster, walkable room, photo to 3D room, clean plate, object cut-out, splat world, physics props, grab and throw, impact sounds, heightfield collision.

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含まれるファイル(4)

  • SKILL.md10.3 KB
  • references/calibration.md3.8 KB
  • references/objects.md2.8 KB
  • references/viewer.md3.0 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

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.

StepDiscoveryWhat to send and check
Uncovernone, read the photo yourselfSingle movable items only, with a normalized box, support, size in meters, materials (references/objects.md)
Plateimg2img, takes a referenceremove the following from the image: ... and nothing else; compare with the photo for over-removal and recoloring
Cut-outimg2img, same memberOne edit per object on the original photo, square, about 1K, white background (references/objects.md)
Worldsearch, single-image worldThe plate, full-resolution splats, a fixed seed, a prompt describing the room without the objects
Objectimg23d with PBROne cut-out per job, PBR on, a moderate face count (about 50,000); pick the GLB by its type (asset_get mimeType model/gltf-binary), never by output order
Soundtxt2audio, sound effectsOne clip of about 6 s per object holding several impacts separated by silence
Calibratenone, localFloor, level, scale, lens, heightfield, placement (references/calibration.md)
Viewernone, localPaged splat streaming, heightfield walker, convex-hull props, grab and throw, panned sounds (references/viewer.md)

Launch every batch with wait=false in waves within the team's concurrency ceiling (the parallel-custom-jobs row in the scenario skill: a launch refused with that 429 created no job, so relaunch it in the next wave), 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.

Common mistakes

  • Treating the world as metric: it is not. Measure the camera's height above the fitted floor and compare the objects' projected heights with their estimated sizes; at authoring time that put the photo camera about 1 m above the floor in every room.
  • Assuming every world shares the photo's lens: restyles of one photo came back with fields of view from about 50 to 76 degrees from the same world model. Fit the lens per world before projecting boxes.
  • Cutting out from the plate: the object is no longer there. Cut-outs come from the original photo.
  • Uncovering plants, glass, or objects holding other objects: image-to-3D closes foliage into a solid shell with leaves painted on. Skip them, or name and remove what they hold.
  • Placing objects from the base of the box on the floor plane alone: objects on tables or seats float or sink. The surface right behind the bottom of the box is where each one stood.
  • Dropping props into physics on page load: noisy supports send them tumbling. Start rigid props asleep where the photo had them; let only soft ones (cushions) settle.
  • Playing a sound on every contact force: a resting object reports its own weight every step. Sound only when the object was moving into the contact.
  • Trusting PBR metalness from image-to-3D: lacquer and glaze come back as metal and render as chrome. Keep metalness only on real metal.
  • Changing the payload after a 3D job reports failure with a result-download error: that is a platform error, not a bad input. Confirm with job_get that the job's status is failure and it has no assets (and that no later job for the same input succeeded, via jobs_list) before retrying once with the same payload; a jobs_wait timeout is not a failure and never justifies a second run.
  • Running heavy local jobs in parallel (calibration, mesh conversion, splat level-of-detail builds): run them one or two at a time.

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

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日本語の概要は準備中です。原文の説明を表示しています。

scenario-labs/skills9922026年10月13日 更新

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scenario-labs/skills9922026年10月13日 更新

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日本語の概要は準備中です。原文の説明を表示しています。

scenario-labs/skills9922026年10月13日 更新

scenario

無料

Use when connecting an AI agent to Scenario (scenario.com) through MCP, or when a task involves generating images, video, 3D, audio, sprites, textures, or game assets. Also when picking a Scenario model, running a LoRA, refining a generation prompt, uploading reference images, waiting on generation jobs, checking credits or quota, hitting Scenario auth, scope, or Forbidden errors, or setting up mcp.scenario.com in Claude Code, Cursor, VSCode, or another agent.

日本語の概要は準備中です。原文の説明を表示しています。

scenario-labs/skills9922026年10月13日 更新

Use when generating or handling 3D assets through the Scenario MCP server, including text-to-3D or image-to-3D meshes, GPT-6 Astra 3D or Claude Opus 5.5 3D, GLB, FBX, OBJ, STL, or VOX files, PBR-textured or game-ready models, voxel models, multi-view reconstruction, retexture, remesh, UV unwrap, auto-rigging a biped or quadruped, or retargeting an animation, previewing a mesh in the inline 3D viewer, or downloading a model for import into Unity, Unreal, Godot, or Blender.

日本語の概要は準備中です。原文の説明を表示しています。

scenario-labs/skills9922026年10月13日 更新

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日本語の概要は準備中です。原文の説明を表示しています。

scenario-labs/skills9922026年10月13日 更新

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