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scenario-rodin

scenario-rodin

github.com/scenario-labsCreative & media1.2k446 installsUpdated 1d ago
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About scenario-rodin

scenario-rodin is a skill for using Scenario’s Rodin Hyper3D models to create 3D assets from images or text, or to split and retexture an existing mesh. It guides creators through choosing model variants, checking schemas, and running jobs.

Key features

  1. 01Image-to-3D and text-to-3D generation
  2. 02Full and Fast model variants
  3. 03Mesh splitting and material regeneration with Bang!
  4. 04Guidance on parameters and model schemas
  5. 05Job polling and asset export workflows

Use cases

  1. 01Create a 3D character from multiple reference views
  2. 02Generate a draft asset from a text prompt
  3. 03Split an existing mesh and regenerate its materials

MCP support

The skill uses Scenario tools such as `search`, `model_schema_get`, `model_run`, and asset management tools through the `scenario` skill.

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/scenario-labs/skills --skill scenario-rodin

After installing, type/scenario-rodinin Claude Code to confirm it loaded.

Details

From the project README

Scenario Rodin 3D

Overview

Rodin Hyper3D, Deemos Technology's 3D family on Scenario, picks its mode by member: image-to-3D and text-to-3D each ship as a full model and a Fast variant, and Bang! splits finished meshes into parts. Discover them with search and treat model_schema_get as the contract: the lanes agree on ideas and disagree on parameter names.

Connection and the core loop: see the scenario skill in this repo; model-agnostic 3D work: the scenario-3d skill. 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

Pick the member by task:

TaskMemberRequired inputs
Image to 3D (img23d)Gen-2.5, or Fastimages
Text to 3D (txt23d)Gen-2.5 Text to 3D, or Fastprompt
Split and retexture (3d23d)Bang!model + image

images is an array even for one still (up to 5 at authoring time), every entry a view of one subject, never an alternative concept. Prompt is optional on the image members: left empty, Rodin writes one from the images. Shared generator knobs: qualityMeshOption (topology and poly budget in one enum string, "18K Quad"), material (PBR, Shaded, All, None), textureDelight (strips baked lighting), TAPose (true poses the character in T or A pose for rigging; the schema does not pick between them), seed (0 to 65535).

The lane decides the dialect

Full and Fast express the same ideas through different parameters, so a payload never moves between lanes unchanged. At authoring time the full lane's tier ran Gen-2.5-Extreme-Low through Gen-2.5-Extreme-High (Extreme High bills double the base rate), HighPack rode in addons as a string (on a Quad mesh it multiplies faces about 16 times), geometryInstructMode: "creative" loosened interpretation, isSymmetric steered symmetry, and isMicro took effect only on Extreme High. The Fast lane's tier stopped at Gen-2.5-Minimum, Gen-2.5-Extreme-Low, and Gen-2.5-Low for one fixed price, highPack was a boolean, enableCreativeMode added generative robustness without losing consistency, and meshes capped at 20K behind an Auto default. In both lanes HighPack means 4K textures plus high-poly geometry at extra cost: dry_run the exact payload before any batch. Text members drop the image-only switches (useOriginalAlpha, previewRender).

Bang! wants a finished mesh

Bang! is 3D-to-3D: it takes an existing 3D asset as model plus a reference image (both required at authoring time, prompt is optional guidance), splits the mesh into semantically meaningful parts, and regenerates each part's materials in the same pass. strength (2 to 12, default 5) sets how fine the split gets, higher splitting into more parts; material defaults to PBR here, not All; resolution "Basic" is 2K, "High" is 4K. At authoring time it ran several minutes and cost more per asset than a default generator run: dry_run it like any other member.

Worked example: a rig-ready character from turnaround stills

  1. search with target="models", query="rodin", public=true. Match capability to task (img23d here, txt23d for prompt-only, Fast for cheap drafts), e.g. model_rodin-hyper3d-v2-5 (a live hit at authoring time: re-discover each session).
  2. model_schema_get with that id: enums, defaults, and caps before anything else.
  3. upload_asset the turnaround stills (see the scenario skill) to get asset ids.
  4. model_run with that model_id, dry_run=true, and parameters={"images": ["asset_front", "asset_side", "asset_back"], "prompt": "stylized adventurer, clean silhouette", "tier": "Gen-2.5-Medium", "qualityMeshOption": "18K Quad", "material": "PBR", "textureDelight": true, "TAPose": true}; re-estimate after any tier or HighPack change.
  5. Re-run with wait=false, then jobs_wait with the returned job id, re-called with pending_job_ids on timeout. Full-lane jobs take minutes; a timeout is not a failure and never justifies a second model_run.
  6. asset_display the mesh, then asset_download for engine import (details in the scenario-3d skill).

Common mistakes

  • A bare string in images: one still goes as ["asset_x"].
  • Carrying a payload across lanes: addons: "HighPack" versus boolean highPack, geometryInstructMode versus enableCreativeMode, disjoint tier enums.
  • Asking the Fast lane for Medium or High tiers or a 500K mesh: its enums stop at Low and 20K.
  • Setting isMicro below Extreme High on the full lane: it changes nothing.
  • Handing Bang! an image as model: model is a 3D asset; the image guides the regenerated textures.
  • Freeform enum strings: values are exact, "Gen-2.5-Medium", "18K Quad", not "medium" or "18k quad".

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