QtMeshEditor-skintokens-onnx

fernandotonon/QtMeshEditor-skintokens-onnx

QtMeshEditor-skintokens-onnx is a other model from fernandotonon. Use it for the other task on the model card, and read the license before you ship it in a product. It is set up for onnx. The card lists the license as mit.

ONNX re-export of VAST-AI SkinTokens / TokenRig (MIT code + MIT weights, Qwen3-0.6B backbone, trained on Articulation-XL2.0 / CC-BY-4.0) — autoregressive ML skin-weight prediction: given a mesh and a skeleton, it pred…

Downloads · 30 days

Access

Public

Updated Jul 10, 2026

Repo size

2.3 GB

Likes

Public

Hugging Face

Repo makeup

Click a slice to open those files.

.data1.7 GB · 72%

At a glance

Task: other
Library: onnx
License: mit
Access: Public
Created: Jul 10, 2026
Updated: Jul 10, 2026
SHA: 4506d42f

Model card

From the Hugging Face model README

SkinTokens / TokenRig — ONNX export

ONNX re-export of VAST-AI SkinTokens / TokenRig (MIT code + MIT weights, Qwen3-0.6B backbone, trained on Articulation-XL2.0 / CC-BY-4.0) — autoregressive ML skin-weight prediction: given a mesh and a skeleton, it predicts per-vertex bone weights. All credit for the original weights goes to VAST-AI-Research.

Exported for QtMeshEditor (issue #819), where it is the default skinner (qtmesh skin, the GUI "Compute Skin Weights" dialog, the rig→skin chain, and the compute_skin_weights MCP tool), running locally via ONNX Runtime with a geodesic-voxel fallback.

The files QtMeshEditor downloads at runtime live in the shared fernandotonon/QtMeshEditor-models repo under skintokens/. This repo is the standalone model card + mirror for people who want the converted weights themselves.

Files (five graphs + manifest)

file	role
`mesh_cond.onnx`	Michelangelo point-cloud encoder → LLM mesh-conditioning prefix
`vae_cond.onnx`	skin-CVAE conditioning encoder over the sampled points
`embed.onnx`	token id → LLM embedding
`decoder.onnx` + `decoder.onnx.data`	Qwen3-0.6B causal-LM KV-cache step (external weights — ONNX Runtime 1.20.1 segfaults parsing a >1.6 GB single-file proto)
`skin_decode.onnx`	FSQ skin tokens → per-joint, per-sampled-point weights (FSQ folded in)
`skintokens.json`	manifest: every config value the host needs (below)

Inference contract

Surface-sample num_points (8192) points + normals; normalise mesh+joints per upstream AugmentAffine (joints included in the AABB, exact [-1,1] fit).
Tokenize the skeleton teacher-forced (DFS order; per bone [branch?, parent-joint xyz, joint xyz] discretised to 256 bins over [-1,1]; bos=257, cls "articulation"=266, stream ends with the switch token eos=258). Multi-root topologies must be re-parented to the first root + DFS-reordered first.
Prefix = mesh-cond embeddings + skeleton token embeddings → autoregressive greedy decode of J × tokens_per_skin (4) skin tokens, constrained to the FSQ range [267, 33035); global EOS 33035; full vocab 33036.
Per joint: skin_decode on its 4 FSQ ids (− 267 offset) → weights over the 8192 sampled points; transfer to full-res vertices by 8-NN inverse-distance.

LLM dims (manifest): hidden 896, 28 layers, 8 KV heads, head_dim 128; tokens_skin_cond=384, CVAE latent 512, FSQ codebook 32768.

Raw predictions are deliberately diffuse — the upstream demo voxel-masks them by default. QtMeshEditor applies a geodesic-localisation pass (filter to geodesically-local bone sets + renormalise); consumers are advised to do the same (bleed 0.74 → 0.05 in our measurements).

Reproducing

scripts/export-skintokens-onnx.py in the QtMeshEditor repo (one-time, offline; bf16→fp32, forced eager attention, decomposed RMSNorm for opset 18, trace-friendly FPS). Parity vs PyTorch ≈ 1e-5 on every graph.

License

MIT (same as the upstream code and weights). Training data: Articulation-XL2.0 (CC-BY-4.0) — credit VAST-AI-Research.

Task: other
Library: onnx
License: mit
Created: Jul 10, 2026
Updated: Jul 10, 2026