UniRig (skeleton stage) — ONNX export
ONNX re-export of the skeleton-prediction stage of VAST-AI/UniRig ("One Model to Rig Them All", SIGGRAPH 2025 — MIT code + MIT weights, trained on Articulation-XL2.0 / CC-BY-4.0): an autoregressive transformer that predicts a full skeleton (joints + hierarchy) from raw mesh geometry — no template or markers needed. All credit for the original weights goes to VAST-AI-Research.
Exported for QtMeshEditor
(issue #408), powering qtmesh rig --algo unirig, the Inspector's
Generate Rig (AI) button, and the auto_rig MCP tool — local inference
via ONNX Runtime with a template-rig fallback.
The files QtMeshEditor downloads at runtime live in the shared
fernandotonon/QtMeshEditor-modelsrepo underunirig/. This repo is the standalone model card + mirror.
Note: UniRig's skin-weight head is not included — it depends on
spconv/PTv3, which has no ONNX lowering. For ML skin weights see
QtMeshEditor-skintokens-onnx.
Files
| file | role |
|---|---|
encoder.onnx |
Michelangelo point-cloud encoder: pc [1,N,3] + feats [1,N,3] (normals), N ≤ 65536 → latent prefix |
decoder.onnx |
~350M-param causal-LM KV-cache step |
embed.onnx |
token id → embedding |
Inference contract
- Surface-sample up to 65536 points + normals, normalise into a centred unit box (+Y up).
- Run the encoder → latent prefix for the LM.
- Greedy constrained autoregressive decode with a manual KV cache, masking each step to the tokenizer FSM's valid next tokens (a documented simplification of upstream's beam+sampling — deterministic, still yields a valid tree). Safety cap 2048 tokens; generation ends at EOS (typically ~180 tokens).
- Detokenize: 256 coordinate bins over
[-1,1],undiscretize(t) = (t+0.5)/256*2-1; vocab 267 (branch=256,bos=257,eos=258); per-bone records[branch?, parent xyz, joint xyz]→ joints + parent indices. Match branch parents to the nearest earlier joint rather than requiring exact bin equality — real decodes frequently miss the exact bin.
Reproducing
scripts/export-unirig-onnx.py in the QtMeshEditor repo (one-time, offline;
via optimum with a hand-rolled KV-cache fallback).
License
MIT (same as the upstream code and weights). Training data: Articulation-XL2.0 (CC-BY-4.0) — credit VAST-AI-Research.