This repository stores the model checkpoints used by the SkinTokens codebase, including:
the FSQ-CVAE that learns the SkinTokens discrete representation of skinning weights, and
the TokenRig autoregressive Transformer (Qwen3-0.6B architecture, GRPO-refined) that jointly generates skeletons and SkinTokens from a 3D mesh.
SkinTokens is the successor to UniRig (SIGGRAPH '25). While UniRig treats skeleton and skinning as decoupled stages, SkinTokens unifies both into a single autoregressive sequence via learned discrete skin tokens, yielding 98%–133% improvement in skinning accuracy and 17%–22% improvement in bone prediction over state-of-the-art baselines.
What Is Included
The repository is organized exactly like the experiments/ folder expected by the main SkinTokens codebase:
The training data (ArticulationXL splits and processed meshes) used to train these checkpoints will be released separately in a future update.
Checkpoint Overview
SkinTokens — FSQ-CVAE (skin-weight tokenizer)
File:experiments/skin_vae_2_10_32768/last.ckpt
Compresses sparse skinning weights into discrete SkinTokens using a Finite Scalar Quantized Conditional VAE with codebook levels [8, 8, 8, 5, 5, 5] (64,000 entries). Used both to tokenize ground-truth weights during training and to decode TokenRig's output tokens back into per-vertex skinning at inference.
Qwen3-0.6B-based Transformer trained on a composite of ArticulationXL 2.0 (70%), VRoid Hub (20%), and ModelsResource (10%), with quantization 256 and 4 skin tokens per bone, then refined with GRPO for 1,400 steps. This is the recommended checkpoint — it generates the skeleton and the SkinTokens in a single unified sequence.
Both checkpoints are required for end-to-end inference: TokenRig generates the rig as a token sequence, and the FSQ-CVAE decoder turns SkinTokens back into dense per-vertex skinning weights.
How To Use
The easiest way is to use the helper script in the main SkinTokens codebase, which downloads both checkpoints and the required Qwen3-0.6B config into the expected layout:
Keep the directory names unchanged. The SkinTokens code expects the exact experiments/.../*.ckpt layout shown above.
TokenRig requires both checkpoints.grpo_1400.ckpt generates discrete tokens; the SkinTokens FSQ-CVAE (last.ckpt) is needed to decode them into per-vertex skinning weights.
Qwen3-0.6B architecture. TokenRig adopts the Qwen3-0.6B architecture (GQA + RoPE) for its autoregressive backbone; the Qwen3 config is fetched automatically by download.py.
Hardware. An NVIDIA GPU with at least 14 GB of memory is required for inference.
Training data. The checkpoints were trained on a composite of ArticulationXL 2.0 (70%), VRoid Hub (20%), and ModelsResource (10%); the processed data splits will be released as a separate dataset repository later.
