Skip to page content
SAVRN
Search Plan a project

Open-weight model · Image to 3d

QtMeshEditor-models

by Fernando Tonon fernandotonon/QtMeshEditor-models

QtMeshEditor-models is an open-weight model for image to 3d from Fernando Tonon, released under other. Its published files total 21.2 GB. It draws 190 downloads a month.

The models used by QtMeshEditor's AI-assisted authoring features. This repo is what the app downloads from at runtime (each model on first use, then it runs locally/offline). Licenses are per model — see the table and each dedicated repo.

Parameters—
Context—
Weights10.4 GB
Licenseother
AccessOpen weights
Monthly Downloads190

Model Card

The models used by QtMeshEditor's AI-assisted authoring features. This repo is what the app downloads from at runtime (each model on first use, then it runs locally/offline). Licenses are per model — see the table and each dedicated repo. The dedicated repos carry the full model cards (I/O contracts, provenance, reproduction scripts) for anyone who wants the converted weights standalone. 1x-PBRifyNormalV3.onnx, 1x-PBRifyRoughnessV2.onnx, 1x-PBRifyHeight.onnx generate tangent-space normal / roughness / height maps from a single albedo texture. ONNX re-exports of the CC0 SPAN models from Kim2091/PBRifyRemix — all RealESRGANx2plus.onnx, RealESRGANx4plus.onnx — 2×/4× super-resolution. ONNX…

Excerpt from the card by Fernando Tonon, licensed other.

Identity and Version

Repository
fernandotonon/QtMeshEditor-models
Publisher
Fernando Tonon
Task
Image to 3d
Modality
Other
Library
onnx
Parameters
Not stated by the source
Languages
pbr
Revision
e8210ddfe0e0bd81764343426f46ccaf0008d081
First published
2026-06-19
Last updated
2026-10-20

Files and Weights

64 files, 21.2 GB in total. The weights are 46 files totalling 10.4 GB in bin, gguf, onnx.

Weights46 files · 10.4 GB
Configuration6 files · 32.9 MB
Documentation6 files · 128.0 KB
Other5 files · 10.8 GB
Repository1 file · 2.1 KB
Every file
FileTypeSizeSHA-256
1x-PBRify_Height.onnxWeights1.6 MB 2b14afd58314
1x-PBRify_NormalV3.onnxWeights1.6 MB 19d78f555f99
1x-PBRify_RoughnessV2.onnxWeights1.6 MB 8a8de1bbeab7
RealESRGAN_x2plus.onnxWeights67.2 MB 35d016524a6e
RealESRGAN_x4plus.onnxWeights67.1 MB c999e5e3365a
caption/SmolVLM-500M-Instruct-Q8_0.ggufWeights436.8 MB 9d4612de6a42
caption/mmproj-SmolVLM-500M-Instruct-Q8_0.ggufWeights108.8 MB d1eb8b6b2397
depth/da2_small.onnxWeights99.1 MB 280ffc23316c
facerig/arkit_template.binWeights17.3 MB f84b22e895dd
facerig/face_landmarks.onnxWeights4.9 MB d16e5a55e6a4
inbetween/rmib.onnxWeights13.2 MB 79bf0f61f91c
mocap/face/face_blendshapes.onnxWeights1.1 MB 37cf54d3ccf6
mocap/face/face_detector.onnxWeights418.4 KB 02a04d5d37c3
mocap/face/face_landmarks.onnxWeights4.9 MB d16e5a55e6a4
mocap/pose/pose_detector.onnxWeights11.9 MB 389df409fbf7
mocap/pose/pose_landmarks.onnxWeights12.8 MB 528137735df0
motion/t2m-v4.onnxWeights17.7 MB 605e979b7acb
motion/t2m-v61.onnxWeights96.6 MB 126ae26f2fde
motion/t2m.onnxWeights96.6 MB eb243c917ecd
pbr/lama.onnxWeights208.0 MB 1faef5301d78
rembg/birefnet.onnxWeights930.5 MB b20ffb0e5c26
rembg/u2net.onnxWeights176.0 MB 8d10d2f3bb75
sam3dbody/mhr_body.onnxWeights13.7 MB 1b5eea828422
sam3dbody/sam3d_backbone.onnxWeights780.1 KB d92e6a0129c5
sam3dbody/sam3d_decoder.onnxWeights254.2 MB b47971ea3f1c
segment/meshseg.onnxWeights784.4 KB 40830bc0d32e
segment/meshseg_building.onnxWeights784.4 KB e2bac77e4e96
segment/meshseg_category.onnxWeights138.8 KB 8222bae09d83
segment/meshseg_vegetation.onnxWeights783.8 KB 951776d4374b
segment/meshseg_vehicle.onnxWeights783.8 KB 4be8e0d1fe63
skintokens/decoder.onnxWeights1.5 MB 86727721ae0a
skintokens/embed.onnxWeights118.4 MB 7f69b5237e0a
skintokens/mesh_cond.onnxWeights116.2 MB f3a8b9a850f2
skintokens/skin_decode.onnxWeights313.7 MB 47ac01547dc4
skintokens/vae_cond.onnxWeights87.2 MB ab54ae6878cc
triposg/triposg_dit_step.onnxWeights1.1 MB ef839b5f5382
triposg/triposg_dit_step_int8.onnxWeights1.4 GB b23bb4ecc658
triposg/triposg_image_encoder.onnxWeights1.2 GB f42cb3ea1315
triposg/triposg_vae_decoder.onnxWeights50.6 MB 6f39b8941522
triposg/triposg_vae_latents.onnxWeights806.4 MB 8fe7a8a700b9
triposr/triposr_decoder.onnxWeights180.7 KB 68ab06fb5ed4
triposr/triposr_encoder.onnxWeights1.7 GB 652b9bfe8851
triposr/triposr_encoder_int8.onnxWeights436.1 MB 0439dd35121f
unirig/decoder.onnxWeights1.2 GB 4b6dcb97eccf
unirig/embed.onnxWeights1.1 MB c84e6bcf78e4
unirig/encoder.onnxWeights216.7 MB 857d23810a51
motion/motion-library-v2.jsonConfiguration27.7 MB 151c1e2611b5
motion/motion-library.jsonConfiguration5.2 MB eb396c1b4245
motion/t2m-vocab-v4.jsonConfiguration412 B —
motion/t2m-vocab-v61.jsonConfiguration1.2 KB —
motion/t2m-vocab.jsonConfiguration1.3 KB —
skintokens/skintokens.jsonConfiguration1.4 KB —
README.mdDocumentation6.1 KB —
facerig/ICT-FaceKit-LICENSE.txtDocumentation1.1 KB —
motion/ATTRIBUTION-v2.mdDocumentation49.9 KB —
motion/ATTRIBUTION.mdDocumentation51.4 KB —
sam3dbody/LICENSE-MHR.txtDocumentation11.4 KB —
sam3dbody/LICENSE-SAM.txtDocumentation8.2 KB —
mocap/NOTICE.txtOther434 B —
sam3dbody/mhr_faces.i32Other442.5 KB f6748e290ef3
sam3dbody/sam3d_backbone.onnx.dataOther3.4 GB 479e7da52d11
skintokens/decoder.onnx.dataOther1.7 GB 8bd78758af8b
triposg/triposg_dit_step.onnx.dataOther5.8 GB 83d53520cfdf
.gitattributesRepository2.1 KB —

License and Download

License
other
Access
Open weights, no gate
Download size
10.4 GB
Download from Fernando Tonon

Released by Fernando Tonon through its official repository on Hugging Face. Read the license.

Memory Requirements

PrecisionWeights in memory
As published10.4 GB

Weights only, from the published parameter count; the key-value cache and runtime add to this.

Questions About QtMeshEditor-models

What license is QtMeshEditor-models released under?

other, as its publisher declares it. Read the license text before commercial use.

Similar Models

Model · Image to 3d

TRELLIS.2-4B

Microsoft

TRELLIS.2 is a state-of-the-art large 3D generative model designed for high-fidelity image-to-3D generation. It leverages a novel "field-free" sparse voxel structure termed O-Voxel and a large-scale flow-matching transformer (4 Billion parameters). Unlike previous methods that rely on iso-surface fields (e.g., SDF, Flexicubes) which struggle with open surfaces or non-manifold geometry, TRELLIS can reconstruct and generate arbitrary 3D assets with complex topologies, sharp features, and full Physical-Based Rendering (PBR) materials—including transparency/translucency. - The CUDA Toolkit is needed to compile certain packages. Recommended version is 12.4. - Conda is recommended for managing…

Open weights mit trellis2

Model · Image to 3d

TRELLIS-image-large

Microsoft

The image conditioned version of TRELLIS, a large 3D genetive model. It was introduced in the paper Structured 3D Latents for Scalable and Versatile 3D Generation.

Open weights mit trellis

Model · Image to 3d

matchanu-1.5

PGR

Matchanu 1.5 is PyGrassReal's generative 3D asset engine capable of synthesizing production-ready watertight 3D meshes (STL, OBJ, PLY) and real-time 3D Gaussian Splats from text descriptions or single 2D concept images. Designed for Industrial Prototyping (3D Printing), Game & AR/VR Assets, and Architectural Elements, Matchanu 1.5 generates clean topology, UV unwrappings, and volume-accurate meshes without the point cloud artifacts common to open-source prototypes. 1. Prompt & Image-to-3D Mesh: - Converts single-view product photos or text prompts into clean, manifold 3D meshes ready for Blender, Rhino, Maya, or Unreal Engine. 2. 3D Printing Ready (Watertight Topology): - Automatically…

Open weights other

Model · Image to 3d

ABot-Recon-Axera

AXERA

ABot-Recon(流式前馈三维重建:视频 → 相机位姿 + 世界坐标点云 + 置信度)编译到 Axera AX650N NPU 的模型文件。 模型与算法来自原作者:amap-cvlab/ABot-Recon(主页 ),本仓库只做 Axera NPU 的格式转换。 代码与使用说明:https://github.com/AXERA-TECH/ABot-Recon-Axera 三个 axmodel 由 pulsar2 编译,芯片 AX650N,支持 AXCL PCIe 卡和 AX650 片上两种运行方式。 打开 http://:8011。Python 调用: 片上运行需要板子 CMM 预留 ≥ 6 GB。 这些权重转换自 ABot-Recon 发布的权重,后者派生自 Pi3;再分发须保留对 Pi3 与 ABot-Recon 的署名与 CC BY-NC 4.0 条款。商业使用需另行获得书面授权。 - presentvalid 封顶 8(应为 11),长序列有轻微漂移。

Open weights cc-by-nc-4.0