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'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 (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
Texture upscaling
RealESRGAN_x2plus.onnx, RealESRGAN_x4plus.onnx — 2×/4× super-resolution.
ONNX re-exports of Real-ESRGAN (xinntao,
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 (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.
Beats spherical-linear interpolation by >2× on held-out CMU motion.
Dedicated repo: 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.
Downloaded into <AppData>/ai_models/segment/. License: CC-BY-4.0.
These models power the AI-assisted authoring features in
QtMeshEditor and its
companion QtMesh Cloud (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.