---
license: cc0-1.0
tags:
- onnx
- pbr
- texture
- normal-map
- qtmesheditor
- qtmesh
- qtmesh-cloud
library_name: onnx
---

# QtMeshEditor — AI models

ONNX models used by [QtMeshEditor](https://github.com/fernandotonon/QtMeshEditor)'s
AI-assisted authoring features.

## PBR map synthesis 

`1x-PBRify_NormalV3.onnx`, `1x-PBRify_RoughnessV2.onnx`, `1x-PBRify_Height.onnx`
generate tangent-space normal / roughness / height maps from a single albedo
(diffuse) texture.

These are **ONNX re-exports** of the CC0 SPAN models from
**[Kim2091/PBRify_Remix](https://github.com/Kim2091/PBRify_Remix)** (LICENSE:
CC0-1.0), trained on CC0 content from ambientCG / Poly Haven. Converted with
`scripts/export-pbrify-onnx.py` in the QtMeshEditor repo (spandrel +
`torch.onnx.export`, opset 18). All credit for the weights goes to Kim2091.

- **License:** CC0-1.0 (public domain), same as the source models.
- **I/O:** 1×3×H×W float NCHW in `[0,1]` → 1×3×H×W out (normal as RGB;
  roughness/height as RGB, consumed as luminance). Dynamic H/W.

QtMeshEditor downloads these on first use into `<AppData>/ai_models/pbr/`.

More info in [QtMesh Cloud website](https://qtmesh.dev)
## Texture upscaling

`RealESRGAN_x2plus.onnx`, `RealESRGAN_x4plus.onnx` — 2×/4× super-resolution.
ONNX re-exports of **Real-ESRGAN** ([xinntao](https://github.com/xinntao/Real-ESRGAN),
**BSD-3-Clause**). Downloaded into `<AppData>/ai_models/pbr/`. Credit: xinntao.

## Auto-rig skeleton prediction (UniRig)

`unirig/encoder.onnx`, `unirig/decoder.onnx`, `unirig/embed.onnx` — ML skeleton
prediction for unrigged meshes. These are **ONNX re-exports** of
**[VAST-AI/UniRig](https://huggingface.co/VAST-AI/UniRig)** (SIGGRAPH 2025 — MIT
code + MIT weights, trained on Articulation-XL2.0 / CC-BY-4.0). Converted with
`scripts/export-unirig-onnx.py` in the QtMeshEditor repo. Downloaded into
`<AppData>/ai_models/unirig/`. Credit for the weights: VAST-AI-Research.

## Animation in-betweening (RMIB) — trained by us

`inbetween/rmib.onnx` — fills the gap between two keyframes with smooth
intermediate motion. **Trained from scratch** by the QtMeshEditor project on the
permissive [CMU Graphics Lab Motion Capture Database](http://mocap.cs.cmu.edu).
Beats spherical-linear interpolation by >2× on held-out CMU motion.
**Dedicated repo:** [fernandotonon/QtMeshEditor-rmib-inbetween](https://huggingface.co/fernandotonon/QtMeshEditor-rmib-inbetween).
Downloaded into `<AppData>/ai_models/inbetween/`. License: CC-BY-4.0.

## Mesh part segmentation — trained by us

`segment/meshseg.onnx` — predicts head / torso / left+right arm / left+right
leg labels per point (PointNet++-style, two kNN aggregation blocks). **Trained
from scratch** (v2) by the QtMeshEditor project on **surface-sampled synthetic
bodies we own** (humanoid incl. chibi, quadruped, biped-with-tail — exact
by-construction labels, CC0) mixed with **CC0 rigged characters mined for
exact rig-derived labels** (Quaternius packs). **94.7%** per-vertex accuracy
on rig-truth eval (v1: 31.5%). **Dedicated repo (full model card + eval data):**
[fernandotonon/QtMeshEditor-mesh-segmentation](https://huggingface.co/fernandotonon/QtMeshEditor-mesh-segmentation).
Downloaded into `<AppData>/ai_models/segment/`. License: CC-BY-4.0.

---

These models power the AI-assisted authoring features in
**[QtMeshEditor](https://github.com/fernandotonon/QtMeshEditor)** and its
companion **QtMesh Cloud** ([qtmesh.dev](https://qtmesh.dev)). Each downloads on
first use and runs locally (offline). Mixed licenses per model as noted above
(CC0 / BSD-3 / MIT-derived / CC-BY-4.0); see each section + the QtMeshEditor
`THIRD_PARTY_AI_MODELS.md`.