Thicken

Photos in. Out comes the object: its 3D model and its metadata.

thicken turns photos of a real object into a GLB model at its real size and its metadata as JSON, read off the object by vision models and looked up in public catalogs. What it knows about each kind of object lives in a profile: a few come with thicken, and you can add your own.

In each, the photos going in and the model coming out, with its metadata, like the one below. The logo above is a thicken model too, made by its lettering profile: thicken lettering thicken .

The other half: its metadata. Part of the first one's, a graded Giannis Antetokounmpo card:

{

"profile": "slab", "holder": "cgc", "grader": "cgc", "cert": "6072896005", "widthMm": 80.5, "heightMm": 135, "depthMm": 7.5, "read": { "grade": "9", "gradeName": "MINT", "autoGrade": "10", "autoGradeName": "AUTO", "signed": "G. ANTETOKOUNMPO, 8/2/25", "name": "Giannis Antetokounmpo", "year": "2024-25", "maker": "Panini", "set": "Court Kings Basketball", "number": "#5", "variant": "Blank Slate" }, "readBy": { "grade": "agreed", "autoGrade": "agreed", "set": "disputed" }, "inside": { "card": { "widthMm": 63.5, "heightMm": 88.9, "corners": "square", "foil": "none" }, "label": { "widthMm": 66.4, "heightMm": 26.9 } } }

The grader and certificate number come from the label's QR code; the vision models read the rest off the slab. What each profile records is in the table of profiles, and every field is described in USAGE.md.

Contents

• Try it · From an AI agent · How it works · The thicken object · What it makes
• In detail (USAGE.md): batch · photos · vision · video · training mode · review · the sidecar · previews · inputs · sizes
• What leaves your machine · Adding a profile · Security · Built on · Development · License

Try it

npm install -g thicken                      # Node 22.18 or later; or npx thicken …

thicken photo.jpg # it works out what it is: a code on it, or the vision models thicken book cover.heic back.heic spine.heic thicken --isbn 9780061120084 # no photo: the cover and size from Open Library thicken slab front.jpg back.jpg thicken card front.jpg back.jpg --pt 130 # a thick patch card thicken badge badge.png --width 50 # cut-out artwork, extruded thicken lettering thicken # the logo thicken batch ~/Pictures/books --preview # a model per photo, PDF, video or folder thicken profiles --json # each profile's name, about and photo-search phrases

Each run writes .glb and .json beside the first picture, and with --preview a picture of both sides. Photos straight on and cropped work as they are; uncropped photos, angled shots and video need a vision roster, any OpenAI-compatible endpoint (Vision). As a library: import { thicken } from 'thicken', then thicken(files, options) gives the GLB's bytes and the sidecar.

From an AI agent

thicken is an MCP server too (thicken mcp), with an Agent Skill that tells the agent when and how to use it: ask for a model of the book on your desk, and the agent makes it. In Claude Code:

/plugin marketplace add bikemeardsley/thicken

/plugin install thicken@thicken

Or for any MCP client, npx -y thicken mcp on stdio. Codex, Copilot, Cursor, Antigravity, Gemini CLI and the rest are in USAGE.md.

How it works

<img src=".github/flow.svg" width="820" alt="Photos, a video, words or an ISBN go in; thicken matches them to a profile, processes them (the vision models find each face, cut it out and straighten it, and read what's printed), builds the object at its real size, and writes a GLB model and a J