img2threejs
Rebuild the object in a reference image as a code-only, procedural Three.js model.
Quality-gated, animation-ready, and deliberately token-efficient — reconstruction-by-code, not photogrammetry, mesh extraction, or downloaded art packs.
Reference images reconstructed in code as animation-ready Three.js models, running live in the browser.
Every model in the gallery is generated code, running in your browser. No mesh files, no downloads.
Live demos
Reconstructions built entirely from primitives, procedural shaders, and generated geometry. Open any model to orbit it, inspect its reference, and read the generated source.
| Demo | Subject | Built with | View | Source |
|---|
| Tropical Island — Code-only Diorama | object | — | Live | code |
| Man Robot · Segment Color Studio | character | — | Live | code |
| Snowbound Crossing — Mini Game | object | — | Live | code |
| Ocean Blue PRS Guitar — Measured Surface Flow | object | v1.5.2 | Live | code |
| Groot — Heart of the Forest | character | v1.5.2 | Live | code |
| Raz — Detonating Strike VFX | character | v1.5.2 | Live |
The newest finished demos, generated from the gallery's
demos.json by scripts/ci/showcase_table.py. The gallery
also has work in progress. Built with is the version tag in each demo's own generatedWith
record (— when it names none).
The gallery source lives in img2threejs/img2threejs-showcase. If this project is useful, a star on this repo helps others find it.
What it does
You give it one reference image of an object. It produces a THREE.Group factory written in TypeScript that recreates that object from primitives, procedural shaders, and generated geometry — with a runtime hierarchy (pivots, sockets, colliders) so the result is ready to animate, not an inert lump.
It runs under Hermes, Claude Code, Codex, or OpenCode. It is agent-agnostic: wherever the docs say "agent vision" or "agent browser tool", it uses whatever the host provides — native image reading, a browser MCP, the project preview, or a user-supplied screenshot.
Subjects and detail accuracy
- Objects and characters. Each subject is classified
object, character, or hybrid. Objects follow the hard-surface pipeline; characters route through an anatomy-aware track (head-unit proportions, facial landmarks, pose) documented in grimoire/character/reconstruction.md.
- Detail-first analysis. Before code generation the pipeline enumerates a
detailInventory of identity-defining small details (gloss, bevel/rounding, screws/rivets, engraved or painted linework, contours, stains and wear). Every detail must map to a real component or material entry, and a strict-quality gate blocks generation until the inventory is complete. Taxonomy: grimoire/intake/detail_inventory.md.
- Maximum likeness for a specific person or character. An opt-in projection-first path fits a parametric template to image landmarks, de-lights the photo, camera-matches the render, and projects the reference onto the mesh. A single image cannot guarantee 100 percent likeness, so the pipeline reports per-region confidence and asks for more views when it matters. Details:
grimoire/character/likeness_maximization.md.
- Multi-view silhouette carving. An opt-in
geometryDescriptor.visualHull intersects at least two deterministic orthographic binary silhouettes into a bounded, welded voxel mesh. It records unseen areas as low-confidence rather than inventing hidden detail. Schema and runtime check: grimoire/scripts.md.
- CS2 weapon review gates. Knife and Glock-18 routes use family-specific component contracts. The review records exactness tier, family identity, painted-region and projection coverage, per-region confidence, approximation notes, and versioned review-scene metadata; component-coverage and map-stripped blockout gates prevent a convincing texture from standing in for real structure. Ships with the CS2 domain plugin; see its
docs/cs2/review-gates.md.
- Resumable local workflow.
forge/state.py records an ordered, evidence-backed intake/pass checklist for the generic profile and every registered domain (in-repo character, plus installed domain plugins such as CS2 and animated-character from plugin-character). forge/next.py --state resumes from that checklist while the existing spec, render, and review gates remain authoritative.
- Material reference pipeline. Every visible material region can be cropped, analyzed, resolved against the versioned Three.js material registry, fitted into
ObjectSculptSpec, rendered from controlled camera views, and accepted only after a per-region comparison gate. See .
How it works
A staged sculpting pipeline turns the reference image into a spec, then generates and vision-reviews one build pass at a time — blockout → structural → form → material → surface → lighting → interaction → optimization — self-correcting until every identity-defining feature clears its threshold. Deterministic Python scripts handle validation and gating; model tokens are spent only on visual judgment and code.
→ Full pipeline diagram, gates, self-correction logic, script reference, and the token-efficiency design: docs/ARCHITECTURE.md
Quick start
Needs Node.js ≥ 18, Git and Python ≥ 3.10, and one of Claude Code, Codex, OpenCode or Hermes.
1. Install the base skill
npx --yes img2threejs@latest install # links the skill into every agent it finds
npx --yes img2threejs@latest doctor # one line per agent: "managed" means installed
Hermes users, also link the skill into your profile (the installer only writes ~/.hermes/skills/):
ln -sfn ~/.hermes/skills/img2threejs ~/.hermes/profiles/${HERMES_ACTIVE_PROFILE:-img2threejs}/skills/img2threejs
Then restart your agent and go to step 4. Prefer to let
your agent install it, want a manual or single-agent install, or something failed:
docs/INSTALL.md (paste-to-agent prompt, expected output, troubleshooting,
update and uninstall, other agents such as Cursor).
2. Install the plugin harness, if needed
Skip this unless you need a plugin from the table below. Plugins come from a separate CLI, the
img2 harness (~/.img2); install it once:
npx --yes @img2threejs/img2@latest install --yes
npx --yes @img2threejs/img2@latest plugins # the live catalog
It links an img2 command if ~/.local/bin, /opt/homebrew/bin or /usr/local/bin is on your
PATH; otherwise use the npx --yes @img2threejs/img2@latest form, or
alias img2='npx --yes @img2threejs/img2@latest'. What it changes on your machine:
docs/INSTALL.md §3.
3. Choose plugins — do not install everything by default
| Plugin / source README | Adds | Access | Install after harness setup |
|---|
| character | Character rigging and animation: skeleton from a GLB, skin conditioning, clip measurement, action design, and a twelve-check rig gate. | Public | img2 add character --yes |
| cs2 | Counter-Strike 2 weapon and glove skin reconstruction: intake contract, authoritative classification, finish/material recipes, and a blocking review gate. | Public | img2 add cs2 --yes |
| environment | Install and manage the private img2 environment plugin through the img2 harness. Authorized GitHub access is required. | Private — authorized Git access required | img2 add environment --yes |
| hello-cube | Turn an image into a deterministic Three.js cube (contract exerciser) | Public | img2 add hello-cube --yes |
| img2glb | Turn an image into a GLB 3D mesh via the hosted TRELLIS space | Public | img2 add img2glb --yes |
The table above is generated from the live catalog (scripts/ci/plugin_table.py); list all
plugins with img2 plugins. Open each plugin's linked README for its input requirements,
dependencies and tool setup. Example (the cs2 plugin; substitute any catalog id):
img2 add cs2 --yes
img2 list
img2 doctor
img2 sync --check
An installed domain plugin contributes its checklist steps, evidence collection, spec
augmentation (quality floors merge raise-only), and blocking review gates. With no plugins
installed, the base has generic and character profiles. A profile whose plugin is
missing (cs2, animated-character) fails loud naming what is installed; it never silently
downgrades. The base skill is not a plugin: do not use img2 add to install this repository.
Private environment plugin: needs a GitHub account with access to its repository and working
Git credentials. The public img2-environment
installer can bootstrap the harness but does not grant that access. Hermes users link each plugin
by hand (docs/INSTALL.md §3). Why a run
cannot see an installed plugin, and how plugins reach the pipeline: docs/PLUGINS.md.
To write a plugin, follow WRITING_A_PLUGIN.md.
4. Run a reconstruction in your agent
Restart the agent (or start a new session) after installing skills.
In Claude Code, attach an image or give its path and invoke:
/img2threejs Rebuild the object in ./reference.png as a procedural Three.js model.
Keep its proportions, silhouette and colours. Follow the quality gates.
Report the output files, render-review evidence and any inferred regions.
In another host, ask the agent to use the installed img2threejs skill with that image.
For a plugin workflow, name the profile explicitly, for example:
Use img2threejs with the installed cs2 domain and ./reference.png.
Follow the cs2 profile and its family-specific reconstruction and review gates.
Use img2threejs with the installed character plugin and ./reference.png.
Follow the animated-character profile. Do not claim animation readiness until its rig gates pass.
The agent validates the image, writes an assessment and spec, generates the factory pass
by pass, and reviews rendered comparisons. Installation alone does none of that.
For a multi-session reconstruction, run these from the skill root:
python3 forge/state.py init --reference <image> --profile character --spec object-sculpt-spec.json
python3 forge/next.py --state .img2threejs/state.json
5. Audit and maintain the installation
npx --yes img2threejs@latest doctor # base-skill host/link report
img2 doctor # static audit of registered plugins
img2 update cs2 --check # read-only check for a newer plugin version
img2 update cs2 # install a newer available plugin version
img2 remove cs2 # unlink owned links and back up the managed checkout
Neither doctor command proves visual likeness or animation quality; those require the
reconstruction's render and domain gates. img2 update --check exits 1 when an update is
pending, not only on errors. Local --link checkouts are live and are not fetched by update.
Symptoms and fixes: docs/INSTALL.md §6; plugin
audit exit codes and replacement behaviour: the
img2 README.
Driving it harder
The one-liner leaves the judgement calls to the skill. When you already know what "correct" means for your subject, say so — each line below maps onto a real gate or artifact in the pipeline, so it changes what gets enforced rather than just adding adjectives:
/img2threejs Rebuild the subject in this image as a procedural Three.js model.
Fidelity Hold proportions and silhouette to the reference. Enumerate the identity-defining
details first — bevels and rounding, panel seams, fasteners, engraved or painted
linework, gloss vs matte zones, wear — and drop any detail you cannot place on a
real component instead of faking it.
Materials Derive the finish class and gradient stops from the reference pixels, not from
memory. Flag any colour that will not survive tone-mapping.
Runtime Expose pivots and sockets for whatever should move, plus a userData.tick for a
looping idle animation.
Gates Run --strict-quality, and do not advance a pass until the side-by-side review
passes. Report per-region confidence for anything the image cannot show.
Useful additions depending on the subject:
- A specific person or character —
Maximize likeness: fit the parametric template to the landmarks, de-light and camera-match the reference, then project it. Tell me which regions are inferred.
- An animal or creature —
This is a creature, not a humanoid — use the quadruped body plan and the body-unit proportion system.
- A saturated anodized or candy finish —
The coat is candy-coat, not gem-metal. Keep the hue; do not let the environment steal it.
- A cost ceiling —
Stay at low effort and skip the presentation composer; I only need the evaluation render.
The scripts run from the skill root and need only Python 3.10+ — nothing to install.
python3 forge/stage1_intake/probe_image.py <image>
python3 forge/stage2_spec/new_pre_spec_assessment.py "Name" --image <image> --out assessment.json
python3 forge/stage2_spec/new_sculpt_spec.py "Name" --image <image> --assessment assessment.json --out spec.json
python3 forge/stage2_spec/validate_sculpt_spec.py spec.json --strict-quality
python3 forge/stage3_build/generate_threejs_factory.py spec.json --out src/createObjectModel.ts
The factory generator repeats the strict-quality gate and is fail-closed: on failure it returns
BLOCKED with the spec artifact, failure metrics, causes, and next action, and does not write a
factory. --allow-nonstrict is only for explicit legacy test fixtures, never production output.
Copy-paste prompts for the GLB-reference route
Rebuilding a character from a GLB reference rather than a photo is a different route with its own
gates — the GLB is a measurement instrument and never ships. Three prompts cover it, each in its own
copy block:
| Prompt | Use it when | Do not use it when |
|---|
| Build | you have a GLB and no built surfaces yet | there is no GLB, or the build already completed and merely looks wrong |
| Polish | the build completed and the result does not look like the GLB | the surfaces were never built — that is a build re-run, not a polish |
| Animation | the figure looks right standing still and has passed the build gates | the surface is ungated, or joint_loops.py fails — that is a surface finding no weight tuning reaches |
They force every parameter the GLB genuinely carries — size, proportions, per-band widths and
centroids, base colour, roughness and metalness — to the measured value, with the check that proves
it landed. Three things are not 1:1 and each prompt says so: rigging and animation are usually
absent from the asset (skinCount: 0, animationCount: 0), texture images and normal maps are
deliberately not copied per this skill's code-only contract, and nothing finer than the node's cell
size can be carried at all.
These are reference material, not a guarantee. They are written to be general, so the measured
figures in them came from one character and are there to show what to measure — not values to copy.
Run the one that matches your situation rather than all three in sequence; each notes the cost of
being used out of turn.
For the script-by-script reference, the full scripts table, and expected artifacts, see docs/ARCHITECTURE.md.
Why it is token-efficient
Most image-to-3D agent loops burn tokens by asking the model to do mechanical work — re-reading the whole model every pass, scoring pixels, validating JSON by hand, re-running steps it already did. img2threejs pushes all of that into deterministic scripts and spends model tokens only where judgment is actually required.
- Scripts enforce, the model judges. The Python scripts handle validation, gating, spec authoring, PBR extraction, comparison-sheet packaging, and pipeline state. They never score visuals. The model's tokens go to one thing: looking at a single side-by-side sheet and deciding pass or fail.
- Zero dependencies, zero install churn. Every script is pure Python 3.10+ standard library. No pip, no PIL, no numpy, no Playwright. PNG read/write is done with
struct and zlib. Nothing to install means nothing to debug in-context.
- Pass-gated generation. The code generator emits only the currently unlocked build pass. The model does not regenerate or re-read the entire model on every iteration — each step is small and scoped.
- Fail fast, before codegen. A strict-quality gate blocks shallow specs before a single line of Three.js is generated, so you never spend tokens rendering a model that was underspecified from the start.
- One image per review. Each pass is judged from exactly one packaged comparison sheet (reference beside render), not a scattering of screenshots.
- Text output, not binaries. The result is diffable TypeScript plus a JSON spec — small, reviewable, and version-controllable, instead of multi-megabyte mesh files.
The net effect: you still get a faithful 3D model from an image, but the expensive model context is reserved for visual judgment and code, not bookkeeping. For the full per-stage and per-cycle token breakdown, see docs/TOKEN_COST.md.
Scripts
| Script | Role |
|---|
stage1_intake/probe_image.py | Image metadata and obvious technical issues (not a visual check). |
stage2_spec/new_pre_spec_assessment.py | Classify the object, score complexity, emit a quality contract. |
stage2_spec/new_sculpt_spec.py | Author the ObjectSculptSpec from the assessment. |
stage2_spec/validate_sculpt_spec.py | Validate the spec; --strict-quality blocks shallow specs before codegen. |
stage1_intake/extract_pbr_evidence.py | Reference-derived PBR evidence per crop (inference, not inverse rendering). |
stage1_intake/material_region_analysis.py | Crop material regions, run texture/PBR evidence, and resolve registry profiles. |
stage2_spec/apply_material_analysis.py | Wire region assignments, priors, maps, and provenance into ObjectSculptSpec. |
stage3_build/orchestrate_passes.py | Locked pass state: status, check, sync. |
stage3_build/generate_threejs_factory.py | Emit the Three.js Group factory for the current unlocked pass. |
stage4_review/material_views.py | Emit multi-angle, zoomed, microscope, environment, and capture-readback contracts. |
stage4_review/material_comparator.py | Compare the visible material crop and classify per-channel mismatches. |
stage4_review/material_feedback.py | Apply bounded, material-scoped corrections through the existing stop policy. |
stage4_review/material_gate.py | Block material-pass until registry, crop, render, compatibility, and comparison evidence passes. |
stage4_review/make_comparison_sheet.py | Package one reference-vs-render sheet for review. |
stage4_review/append_review.py | Record a per-pass review: scores, decision, evidence. |
_shared/feature_acceptance_policy.py | Internal helper enforcing per-feature score thresholds. |
stage1_intake/build_detail_inventory.py | Slice the reference into zones and scaffold a detail inventory. |
| stage5_rig/rig_spec.py | Derive and validate a skeleton from the component tree, so bones cannot drift from the geometry. |
| stage5_rig/geodesic_skinning.py | Vertex weights from distance measured through the solid; keeps rigid roles out of smooth skinning. |
| stage5_rig/validate_rig_payload.py | Blocking payload-integrity gate before a THREE.Skeleton is bound. |
| stage1_intake/extract_hair_evidence.py | Hair/skin split, banded coverage, hairline, highlight band, root-to-tip delta. |
| stage4_review/scalp_exposure.py | HARD gate: finds bald patches on geometry, before any render. |
| stage4_review/hair_gate.py | Soft gate: hair coverage, hairline and highlight offsets against the reference. |
| stage4_review/interior_difference.py | Appearance difference inside the silhouette, banded by height. Required per visual pass. |
| _shared/chirality.py | Left/right as an importable convention, with the two gates the two chirality defects need. |
| _shared/pipeline_routing.py | Fail-closed weapon/character routing; low confidence resolves to request-input. |
This is a curated selection — forge/ holds around ninety modules. The executable reference with every flag is grimoire/scripts.md, and the gate-by-gate contract is grimoire/review/gates_reference.md. The rest of grimoire/ holds the rubrics each gate applies (validation, pre-spec assessment, procedural patterns, material and lighting realism, attachment correctness, action-ready models, self-correction).
Optional reference-fidelity tooling
The stdlib-only core can use an isolated evidence layer without taking on runtime dependencies:
SAM2 component masks, Depth Anything V2 relative-depth priors, MediaPipe face/pose landmarks,
Chrome DevTools diagnostics, Three.js scene inspection, Playwright cross-browser fallback, and
version-aware Context7 retrieval. These tools never approve a pass or silently provide geometry.
Install, routing, provenance rules and exact commands:
docs/integrations/reference_fidelity_tooling.md.
Optional GLB-baseline character pipeline
integrations/glb_character_pipeline reconstructs a character
from a multipart GLB used as a measurement instrument plus its diffuse image, and emits the
procedural TypeScript the demo actually ships — no .glb or .bin is fetched at runtime. It carries
its own pyproject.toml/uv.lock so the stdlib-only forge core stays dependency-free, and operates
on a companion showcase checkout via IMG2THREEJS_SHOWCASE_ROOT.
It applies only when a build has a GLB to measure — skip it entirely otherwise, and use the core
image-driven pipeline instead. Reproduces girl-character's shipped crossSections.ts exactly (748
rings, 86,240 ring points). Method and per-stage rationale:
PIPELINE.md.
What you get
- An
ObjectSculptSpec JSON: the full component tree, materials, repetition systems, sockets, and a recorded review history for every pass.
- A TypeScript
createObjectNameModel(spec, options) factory returning a THREE.Group, with root.userData.sculptRuntime exposing nodes, sockets, colliders, and destruction groups.
- For character builds,
root.userData.rig: bones, one shared Skeleton, bone order and index map, and a bound flag computed from whether every skinned mesh actually bound.
- A render plus comparison sheets documenting the fidelity at each pass.
For the script-by-script reference and the full list of output artifacts, see docs/ARCHITECTURE.md.
Roadmap
Shipped:
- v1.0 — object pipeline: staged sculpt, render-vs-reference review loop, action-ready hierarchy.
- v1.1 — detail-first analysis: required detail inventory, strict-quality gate.
- v1.2 — humanoid character generator: anatomy track, proportion-lock and feature-placement passes.
- v1.3 — quality & efficiency: the Divine Eye deterministic review harness, input-integrity and geometry-truth gates, reference-grounded texture and gradient analysis, CIEDE2000 colour math.
- v1.4 — The Weapon Update — CS2 image-matched reconstruction: provenance-aware intake, projection-first finishes, family-specific weapon adapters, and structural review gates.
- v1.4.1 — CS2 hardening: explicit component coverage, a dedicated Glock-18 assembly contract, map-stripped blockout evidence, and stricter geometry-integrity checks.
- creature generator — 4 body plans (quadruped / avian / winged-dragon / serpentine),
animalAnatomy spec, spine-loft geometry, ΔE00 colour gates.
- v1.5 — The Character Update — a skeleton derived from the component tree and bound to
SkinnedMesh geometry, geodesic skinning, hair as a five-stage subsystem with a hard scalp-exposure gate, chirality gates, interior-difference review, the tapered-sweep primitive, the material pipeline with a blocking acceptance gate, and resumable workflow state. Not included: the hairProfile compiler, IK, pose-sweep gating, clothing.
- v2.0 — The Plugin Update — the domain registry, pull-based spec augmentation
with raise-only quality floors, the emission-target socket with provenance, per-plugin blocking
gates, and the img2 harness (
img2 install/add/doctor). CS2 extracted into plugin-cs2,
animated-character served by plugin-character; the base names no domain. The "plugin
ecosystem and API" originally slotted for the Procedural World bundle, shipped first as its own major.
Next — one theme per release:
- v2.1 — The Character Split: the in-repo
character domain joins plugin-character, so the base names no domain and the v2.0 plugin split closes.
- v2.2 — The Environment Update: buildings, rooms, streets, vegetation, terrain-aware and multi-object reconstruction.
- v2.3 — The Game Pipeline Update: Unity and Unreal exporters, a Blender bridge, LOD and collision-mesh generation.
- v2.4 — The Animation Update: auto rigging, auto skin weights, Mixamo compatibility, facial rig.
- v2.5 — The AI Studio Update: web UI, batch processing, visual prompt builder, cloud rendering.
- v3.0 — The Procedural World Update: multi-view reconstruction, procedural city generation, semantic world understanding.
The arc: assets (v1.4–v1.5) → the plugin ecosystem (v2.0–v2.1) → worlds (v2.2–v2.3) → production (v2.4–v2.5) → an AI game-asset platform that generates playable worlds from reference images (v3.0).
→ Full roadmap — per-version detail, the four-phase long view, and the tracked capability gaps: ROADMAP.md. Technical specification: docs/UPGRADE_PLAN.md.
Honesty about limits
A single image cannot reveal hidden sides or guarantee exact geometry. The skill states plainly when output is approximate, stylized, or low-poly, and infers unseen faces by mirroring visible ones rather than faking confidence. It is strong for hard-surface objects; characters are stylized reconstructions, not photoreal likeness. "This cannot reach the requested fidelity from this image" is a valid, expected result.
Star history
If img2threejs is useful to you, a star helps others find it.
Support the project
img2threejs is free and open source. If it saved you time or found its way into your project, consider supporting continued development:
VietQR / MoMo / PayPal also work — see the donate page.
Sponsors
Reconstruction-by-code is an inference workload before it is a graphics one: every gate rerun, every
render-vs-reference pass and every material fit spends tokens. These four pay for that loop.
MiniMax
MiniMax is a global AI foundation model company founded in early 2022, committed to advancing the frontiers of AI towards AGI under the mission "Intelligence with Everyone." MiniMax has independently developed a family of multimodal foundation models with strong code and agent capabilities plus ultra-long context processing — spanning text, audio, image, video, and music. The same company publishes MiniMax Code, MiniMax Design, MiniMax Audio and Talkie, and serves a token-plan API platform for developers and enterprises at https://platform.minimax.io/subscribe/token-plan. Check the token plan for budget-friendly access to the M3, M2.7, M2.5, H3, Speech 2.8 and Music 3.0 model families.
Atlas Cloud
Atlas Cloud is a full-modal AI inference
platform: one AI API for video generation, image generation and LLM access, with unified access to
300+ curated models across every modality instead of a separate integration per vendor.
That single endpoint is what keeps this project's loop affordable — the pipeline is token-hungry by
design, because gating a spec before codegen means running the analysis more than once. Atlas Cloud's
coding plan is the budget-friendly route to that API.
→ Open the Atlas Cloud coding plan
Tripo
Tripo turns a prompt or a reference image into a production 3D asset:
High Detail meshes up to 2M polygons for render and print, artist-grade quad Smart Mesh from 500
to 50K triangles for real-time engines, AI auto-rigging, PBR texturing up to 8K, and part-level
segmentation. Exports GLB, FBX, OBJ, USD, STL and 3MF, with first-party plugins for Blender, Unity,
Unreal, Godot, Cocos and ComfyUI.
It pairs with img2threejs as the measuring stick. A procedural rebuild lives or dies on silhouette,
proportion and joint placement, and a quad mesh plus an auto-rig of the same subject gives those
gates a second read that one reference photograph cannot settle on its own.
→ Open Tripo Studio
Hyper3D
Hyper3D builds Rodin, which generates a 3D asset from a prompt or an
image in seconds with high reference fidelity and coherent detail across views. Generation is
steerable rather than a dice roll: bounding-box, voxel and point-cloud ControlNet guidance, partial
editing to refine one region without disturbing the rest, smart low-poly optimisation, and
ChatAvatar for rigged production faces. Exports STL, FBX, OBJ, GLB, glTF and USDZ.
It answers the one question a single photograph never can — what the back looks like. Generate the
subject, orbit it, and the hidden sides become references the material and surface gates can actually
be run against, instead of assumptions the pipeline has to make silently.
→ Open Hyper3D Rodin
Sponsorship pays for compute, not for coverage: a sponsor's product is described here in its own
terms, and no gate, default or benchmark in this repository is weighted toward one. Want your logo in
this row? Open an issue or write to [email protected].
Contributing
Contributions are welcome — procedural material recipes, new gates, host coverage, and demos especially. See CONTRIBUTING.md and the roadmap for where the project is headed.
License
Apache License 2.0. See LICENSE.
