img2threejs

name	img2threejs
description	Turn an object or character reference image into a quality-gated, animation-ready procedural Three.js model built in code. Use for image-to-3D reconstruction, detail-accurate object rebuilds, stylized/likeness-maximized human characters, sculpt specs, and staged code generation.
license	Apache-2.0
version	1.3.0

img2threejs — Image to procedural Three.js

Rebuild the object visible in a reference image as a code-only procedural Three.js model, gated by a staged sculpting pipeline and an AI-vision self-correction loop. This is reconstruction-by-code, not photogrammetry, mesh extraction, or downloaded art packs.

Agent-agnostic: works under Claude Code, Codex, or OpenCode. Wherever this doc says "agent vision" or "agent browser tool", use whatever the host provides — native image reading, a browser MCP (playwright/chrome-devtools), the project preview, or a user-supplied screenshot.

When To Use

The user attaches/points to an object image and wants a procedural Three.js model, a reconstruction/animation/destruction plan, a sculpt spec, or code. Also for material studies, action-ready props, game objects, botanical/mechanical parts, and stylized reconstructions.

Core Promise

Sculpt from a photo, in order — never one-shot a mesh:

1. Validate the image is a suitable 3D target (grimoire/intake/validation_rubric.md).
2. Assess object class + complexity, then write a qualityContract before any code.
3. Spec it: component hierarchy, materials, lighting, pivots, sockets, action anchors.
4. Build pass-by-pass from blockout → structure → form → material → lighting → interaction → optimization.
5. Verify each pass with a screenshot compared against the reference; fail a pass if an identity-defining feature is wrong even when the global score looks fine.

State explicitly when output is approximate/stylized/low-poly. A single image cannot reveal hidden sides or guarantee exact geometry — say so instead of faking confidence.

Required Inputs

• one image path / screenshot / URL / attached image (if missing or unreadable, ask)
• intended use: prop, game object, hero render, playable/destructible object, animation rig (default: real-time browser prop with interactive performance)

The Loop (scripts do enforcement; agent vision does judgment)

Run scripts from the skill root (forge/...). Pure Python 3.10+ stdlib, no pip installs. Full flags: grimoire/scripts.md. Never let a script score visuals — that is the agent's job.

1. Probe local images: forge/stage1_intake/probe_image.py <image> (metadata only, not a visual check).
2. Pre-Spec Assessment Gate — classify + score complexity + write the quality contract: forge/stage2_spec/new_pre_spec_assessment.py "Name" --image <img> --complexity <simple|moderate|complex|ultra-complex> --out assessment.json. Rules: grimoire/intake/quality_contract.md. Set objectClass.primaryDomain (object | character | hybrid) and fill the seeded detailInventory (its targetMinDetails scales with complexity). 2b. Detail inventory (do not skip for detailed subjects) — scan zones and enumerate every identity-defining small detail (gloss, bevel, fasteners, linework, contours, stains): forge/stage1_intake/build_detail_inventory.py <image> --mode grid-3x3 --out-dir <dir> --out di.json. Each detail MUST map to a component.localFeatures or material.localOverrides entry — never prose only. Taxonomy + 3D-term recipes: grimoire/intake/detail_inventory.md. 2c. Character/hybrid subjects — capture head-unit proportions + facial/body landmarks: forge/stage1_intake/extract_landmarks.py <image> --out anatomy.json --overlay overlay.png, then fill preSpecAssessment.anatomy. Route: grimoire/character/reconstruction.md. For maximum likeness use the projection-first path (grimoire/character/likeness_maximization.md): solve the camera (stage1_intake/solve_camera_pose.py), de-light the photo (stage1_intake/delight_albedo.py), and project it onto the fitted mesh (stage3_build/bake_projected_texture.py). A single image cannot guarantee 100% likeness — report per-region confidence and request more views for a real person.
3. Author the spec from the assessment: forge/stage2_spec/new_sculpt_spec.py "Name" --image <img> --assessment assessment.json --out object-sculpt-spec.json. Replace generic starter featureReviewTargets with the object's real identity-defining systems (≤5 critical, ≤3 important per pass); for characters add anatomy-proportion, face-landmark-placement, pose-silhouette, outfit-and-palette. Use 3D-graphics terms only (grimoire/glossary/3d_vocabulary.md), never "nice/smooth/shiny". Classify every component's / per before picking a — this is what prevents a continuous organic form from being picked as a box.

Gates (do not skip)

• Suitability + reference integrity: pass / conditional / reject before any planning (grimoire/intake/validation_rubric.md), AND every reference admitted via forge/stage1_intake/check_reference_admission.py (rejects empty/fragmented/tiny/duplicate/ undecodable refs with a reason). Intake understanding cross-checked by forge/stage1_intake/check_intake_correctness.py (halts on a confident class contradiction).
• Divine Eye (the harness heart) — deterministic-first, model-last: the render evaluator is forge/stage4_review/divine_eye.py — a zero-token multi-signal ensemble (IoU/scale HARD gates; proportion/symmetry-parity/pHash/SSIM/edge/blowout/flat/tonal-parity soft) with self-uncertainty (probe on signal disagreement) and deterministic routing (continue/refine-spec/refine-code/ probe). The VLM (forge/stage4_review/vlm_gate.py) is a gated, calibrated, cross-checked last layer: never consulted on a hard-gate failure, multi-sample-voted, and can rescue a soft near-threshold reject but never grant past a hard geometric failure.
• Multi-angle or it didn't happen: a non-planar form must hold from ≥2 camera angles. forge/stage4_review/diagnose_render_multi_angle.py flags degenerate-view when an orbited silhouette collapses (a flat plane faking a volume). Orbit angles use reference-free self-consistency — never scored against a reference angle the photo doesn't cover.
• Bounded correction loop (token-burn safety): forge/stage4_review/correction_loop.py guarantees termination (success/repeated-defect/oscillation/plateau/hard-ceiling), escalating to request-input — never a silent infinite burn.
• Tier 1 (legacy, still valid): "Tier 2 (AI-vision) never runs against a render that has not passed Tier 1." Run forge/stage4_review/diagnose_render.py (silhouette IoU/proportion/symmetry/per-part color) and record it (--spec ... --in-place) before requesting a comparison sheet; orchestrate_passes.py check refuses otherwise.
• Pre-spec / strict-quality: blocks code gen until the spec is deep enough for its contract.
• Screenshot feedback: continue is allowed only with a render + comparison sheet + global AI-vision score ≥ threshold (default 0.7) AND every critical feature ≥ its own threshold. Details + per-layer scorecard: .

Self-Correction

After every pass, decide exactly one: continue | refine-spec | refine-code | request-input | stop. refine-spec fixes a wrong/missing/shallow spec (re-validate, don't patch code around it); refine-code fixes geometry/material/lighting that doesn't match a sound spec. Full root-cause guide + fidelity scale: grimoire/review/self_correction.md.

Implementation Rules (brief)

TypeScript + plain Three.js unless the project uses a wrapper. Group factory createObjectNameModel(spec, options), reconstruction data kept separate from renderer objects, deterministic seeds for all procedural noise. Prefer primitives / Shape extrude / curve+tube / instancing / displacement / generated canvas textures before any external art. Full geometry & material recipes + hard-won failure patterns: grimoire/build/geometry_patterns.md.

Output

• Analysis-only: suitability verdict + scores, object extraction, macro→micro hierarchy, geometry strategy, material/lighting recipe, animation/destruction feasibility, plan + risks.
• Implementation: the above briefly, then edit code; verify with typecheck/build + a screenshot.
• Not feasible: name the blocker, ask for more views / cleaner image / accepted stylization / a narrower target. "This cannot reach the requested fidelity from this image" is a valid result.