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Reference To 3d

Community
RobLe3
reference-to-3d

Reconstruct Blender models from supplied reference sheets, branding templates, texture atlases, orthographic front/side/back/top views, or mascot/logo art where visual fidelity to the source is more important than a plausible generated object. Use when the user says the model must match a template, wireframe, texture pack, character sheet, mascot sheet, or brand asset exactly; also use after feedback like "does not look like the reference", "fit the texture 1:1", "wrong number of visible parts", or "compare against the template". Requires Blender MCP plus local Python with Pillow/OpenCV/numpy; pairs with blender-uv-texturing, wireframe-to-3d, blender-modeling, blender-materials, and blender-export.

Overview

PublisherRobLe3
Repositorycc-blender-skill
Skill namereference-to-3d
Stars
131
Forks
14
Bundled files
4
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by RobLe3 on GitHub. Read the source before you install it.

Installation

Install the Reference To 3d AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/RobLe3/cc-blender-skill/tree/main/plugin/skills/reference-to-3d
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/RobLe3/cc-blender-skill.git /tmp/cc-blender-skill
mkdir -p .claude/skills
cp -r /tmp/cc-blender-skill/plugin/skills/reference-to-3d .claude/skills/reference-to-3d
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Reference To 3d in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Reference To 3d on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Reference To 3d is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

MCP backend

Use when: User supplies reference images/templates/texture atlases/orthographic views and wants a model matching them, or repeated visual mismatch feedback requires a reference-locked corrective loop.

Before the first Blender call, read ../text-to-blender/references/mcp-backends.md. Prefer official MCP for shared capabilities; preserve the selected backend for the whole task. Tool names in recipes are client/legacy examples: map them through that reference and its agent-host guide.

Reference-to-3D Reconstruction

This skill is for source-locked modeling: the reference asset is the contract. Do not generate a plausible object from memory and then decorate it. Extract measurements, part counts, silhouettes, and UV regions from the supplied files first, then build the Blender model to satisfy those measurements.

1:1 reconstruction upgrade

When the user requires true 1:1 matching, chain these skills instead of attempting another plausible modeling pass:

  1. reference-analysis-validator for manifest, masks, part counts, overlay gates.
  2. orthographic-registration for front/side/back/top coordinate agreement.
  3. contour-to-mesh for silhouette-derived structural meshes.
  4. atlas-uv-fitting for per-part texture/UV fitting.
  5. mascot-logo-reconstruction as the full orchestrator for brand mascot/logo work.

Do not export a final asset until the validation JSON and overlay render pass the manifest thresholds.

Triggered failure mode

If the user says the output does not match the templates/textures/wireframes, stop regular text-to-blender generation and enter this workflow. Repeated plausible-but-wrong renders usually mean the missing step is reference analysis, not another modeling iteration.

Source-of-truth hierarchy

  1. Front template / front wireframe: primary silhouette, visible part count, face/feature positions, brand read.
  2. Texture atlas: exact part shapes, color/material boundaries, decals, lightmap/aura intent.
  3. Side/back/top views: depth, stacking order, thickness, backside forms. They must not change the locked front projection.
  4. User feedback: hard constraints; promote it into validation gates.

Mandatory preflight checklist

Before Blender modeling, write a small reference_manifest.json with:

json
{
  "source_files": [],
  "primary_view": "front",
  "expected_primary_parts": {"count": null, "labels": []},
  "structural_parts": [],
  "decorative_parts": [],
  "texture_regions": [],
  "validation_thresholds": {
    "front_mask_iou_min": 0.90,
    "bbox_center_tolerance_px": 12,
    "part_count_exact": true
  }
}

If the expected part count is unclear, derive it from the reference sheet with the analyzer and show the user the uncertainty instead of guessing.

Pipeline

1. Inventory and classify sources

Classify each provided asset:

  • front_template: hero brand image or front wireframe.
  • side_view, back_view, top_view: orthographic depth references.
  • texture_atlas: basecolor / albedo atlas containing pieces.
  • decal: transparent face/expression or detail layer.
  • emissive, roughness, bump, normal, lightmap: material maps.
  • aura/background: optional visual context, not structural body geometry.

2. Analyze images locally

Use scripts/template_analyzer.py for first-pass component extraction:

bash
python3 ${CLAUDE_SKILL_DIR}/scripts/template_analyzer.py   --image /path/to/front_reference.png   --out /tmp/front_analysis.json   --mode edges  # use bright_on_dark/dark_on_bright/alpha when cleaner

The analyzer returns components with area, perimeter-derived score, bbox, centroid, contour, and approximate class hints. Use --mode edges for faint grey wireframes and Otsu modes for clean masks/atlases. Treat this as measurement input, not final authority.

3. Lock the front reference in Blender

Create a front reference plane or Image Empty in the same orthographic camera used for validation. Prefix it REF_ and exclude it from export. Official Blender docs describe Image Empties as reference images/blueprints and support front/back depth, opacity, orthographic-only display, and axis-aligned display.

4. Build geometry from source contours

For each structural part:

  1. Convert the 2D contour to front-view X/Z coordinates.
  2. Generate a quad strip/grid inside that contour.
  3. Assign Project-from-View-style UVs based on front X/Z bounds or exact atlas region.
  4. Add only shallow Y-depth/crown/extrusion after the front silhouette is locked.
  5. Keep part names semantic: GEO-subject_part_top, GEO-subject_face_shell, etc.

Do not radial-duplicate a mascot unless the reference is truly radial. Logos often have occlusion, intentional asymmetry, and a fixed visible part count.

5. Multi-view registration

  • Front X/Z is locked first.
  • Side view controls Y/Z depth envelope only.
  • Top view controls X/Y spread only.
  • Back view controls hidden/backside geometry and validation, not front silhouette.

If views disagree, keep the front brand read and document the conflict.

6. UV and texture mapping

Chain-load blender-uv-texturing when any texture pack is provided. For atlas-driven parts:

  • crop or map per atlas region; do not project the whole atlas onto every part;
  • front-facing hero surfaces use front-projected UVs;
  • side/back surfaces use side/back UV islands, procedural fill, or baked colors;
  • alpha decals need transparent material settings and must not introduce black planes;
  • lightmaps are optional realtime helpers and should not be connected as emission unless specified.

7. Validation gates before export

Always produce:

  • front_preview.png
  • front_overlay_reference.png
  • front_mask_validation.json
  • side_preview.png
  • back_preview.png
  • top_preview.png

Use scripts/silhouette_validator.py:

bash
python3 ${CLAUDE_SKILL_DIR}/scripts/silhouette_validator.py   --reference /tmp/reference_mask.png   --render /tmp/render_mask.png   --out /tmp/validation.json

Refuse final export if:

  • primary part count differs from manifest;
  • front-mask IoU is below threshold;
  • face/feature centroid is outside tolerance;
  • optional aura/decorations are included in the base GLB by mistake.

Blender code patterns

Reference plane in front-view X/Z

python
import bpy

def create_reference_plane(name, image_path, width, height, y=0.2, alpha=0.35):
    img = bpy.data.images.load(image_path, check_existing=True)
    mat = bpy.data.materials.new('MAT-' + name)
    mat.use_nodes = True
    nodes = mat.node_tree.nodes
    bsdf = nodes.get('Principled BSDF')
    tex = nodes.new('ShaderNodeTexImage')
    tex.image = img
    mat.node_tree.links.new(tex.outputs['Color'], bsdf.inputs['Base Color'])
    mat.node_tree.links.new(tex.outputs['Alpha'], bsdf.inputs['Alpha'])
    bsdf.inputs['Alpha'].default_value = alpha
    mat.blend_method = 'BLEND'
    verts = [(-width/2,y,-height/2),(width/2,y,-height/2),(width/2,y,height/2),(-width/2,y,height/2)]
    mesh = bpy.data.meshes.new(name + 'Mesh')
    mesh.from_pydata(verts, [], [(0,1,2,3)])
    mesh.update()
    uv = mesh.uv_layers.new(name='UV')
    for li, uvco in zip(mesh.polygons[0].loop_indices, [(0,0),(1,0),(1,1),(0,1)]):
        uv.data[li].uv = uvco
    obj = bpy.data.objects.new('REF-' + name, mesh)
    bpy.context.collection.objects.link(obj)
    obj.data.materials.append(mat)
    obj.hide_render = True
    return obj

Validation print contract

python
print('VALIDATE expected_primary_parts=', expected, 'actual=', actual)
print('VALIDATE front_locked=True reference_overlay_rendered=True')
if actual != expected:
    raise RuntimeError('Part-count mismatch; refusing export')

When to stop and ask for human input

Ask for a correction only after generating evidence, e.g. an overlay image or JSON showing ambiguity. Do not ask vaguely. Example: “The analyzer finds N prominent structural components plus M faint background/decorative shapes. Should the GLB include only the structural components?”

Sources distilled

  • Blender Manual: Image Empties for reference images/blueprints; front/back display, opacity, orthographic and axis-aligned controls.
  • Blender Manual: Trace Image to Grease Pencil works best from manually prepared black/white images and controlled resolution.
  • Blender Manual: Shrinkwrap moves vertices to a target surface and is useful after silhouette lock for conforming secondary details.
  • Blender Manual: UV/Image Texture and Project-from-View-style workflows require UV maps and material nodes for renders/exports.
  • OpenCV docs: contour moments, area, perimeter, bounding boxes, template matching.
  • scikit-image docs: SSIM for image similarity when pixel-MSE is not perceptually meaningful.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Reference To 3d AI skill do?

Reconstruct Blender models from supplied reference sheets, branding templates, texture atlases, orthographic front/side/back/top views, or mascot/logo art where visual fidelity to the source is more important than a plausible generated object. Use when the user says the model must match a template, wireframe, texture pack, character sheet, mascot sheet, or brand asset exactly; also use after feedback like "does not look like the reference", "fit the texture 1:1", "wrong number of visible parts", or "compare against the template". Requires Blender MCP plus local Python with Pillow/OpenCV/num...

Why use Reference To 3d on TypingMind?

Because you install it once and use it with any model. Reference To 3d is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Reference To 3d in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/RobLe3/cc-blender-skill/tree/main/plugin/skills/reference-to-3d. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Reference To 3d?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Reference To 3d?

As many as you like. As long as a model supports skills, you can use Reference To 3d with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Reference To 3d AI skill free?

Yes. It is published on GitHub by RobLe3 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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