Detalle del Skill
reference-to-3d
Useful specialized reconstruction workflow for reference-based modeling.
Revisar antes de usar
La revisión automática comprueba relevancia, no seguridad ni respaldo. Lee las instrucciones de la fuente antes de usar este Skill.
SKILL.md
Este extracto es una copia guardada durante la revisión. La fuente externa contiene la versión completa y actual.
---
name: reference-to-3d
description: 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.
when_to_use: 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.
allowed-tools: Read Bash Glob Grep mcp__blender__execute_blender_code mcp__blender__get_scene_info mcp__blender__get_object_info mcp__blender__get_viewport_screenshot
---
# 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:
```bLeer la fuente completa en GitHub (abre una página externa)