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dexgarment-dense-descriptors-20260826
Cloth-splatters/dexgarment-dense-descriptors-20260826
dexgarment-dense-descriptors-20260826 is a machine learning model from Cloth-splatters. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.
This repository contains all 48 trained seed-0 checkpoints from the final DexGarment dense-correspondence comparison. Models cover two observation regimes (flat and deformation), eight recipes, and three garment categ…
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Updated Sep 10, 2026
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At a glance
- License
- mit
- Access
- Public
- Created
- Sep 10, 2026
- Updated
- Sep 10, 2026
- SHA
- 13c42b7e
Model card
From the Hugging Face model README
DexGarment dense descriptor model matrix (2026-08-26)
This repository contains all 48 trained seed-0 checkpoints from the final
DexGarment dense-correspondence comparison. Models cover two observation
regimes (flat and deformation), eight recipes, and three garment categories
(tops, dress, and trousers).
These are point-cloud descriptor models for garment correspondence. They are not cloth state estimators or dynamics models.
Layout
models/<regime>/<recipe>/<category>/seed_0/
best.pt
config.json
validation_metrics.json
test_metrics.json
comparison/
summary.json
runs.csv
experiment/
marlowe_matrix.json
SHA256SUMS
The eight trained recipes are:
gam_scratch_sharedunigarment_scratch_sharedgam_pretrained_adaptunigarment_native_coarsegam_shared_c2fgam_pretrained_c2funigarment_shared_c2funigarment_native_c2f
The separately released, unadapted GAM baseline is not duplicated here. Its source checkpoints remain part of DexGarmentLab/Model-HALO rather than outputs of this training matrix.
Recommended checkpoints
Macro averages are over tops, dress, and trousers.
| Use case | Recipe | Dense error | Dense <=2 cm | Exact top-1 |
|---|---|---|---|---|
| Flat correspondence | unigarment_shared_c2f | 0.613 cm | 95.365% | 44.375% |
| Deformation correspondence | unigarment_native_c2f | 2.208 cm | 59.890% | 14.305% |
For deformation semantic metrics, unigarment_scratch_shared is strongest in
this comparison (4.531 cm semantic error and 69.576% within 5 cm).
Loading
Use the pinned UniGarmentManip model builder and checkpoint loader:
from dexgarment_adaptation.model import build_descriptor_model, load_adapted_checkpoint
model = build_descriptor_model("unigarment", dgl_root, feature_dim=512)
load_adapted_checkpoint(model, checkpoint_path, "cuda:0")
model = model.to("cuda:0").eval()
Select gam or unigarment from the recipe name. The exact architecture and
training settings for every checkpoint are recorded in its adjacent
config.json.
Provenance and scope
- UniGarmentManip experiment commit:
0701bc475877da68bc07f727a1995fd78a3de511 - Code fingerprint:
665d3f5e2509bc26ea46fb467d53056a0fcea09998e9d0955e027c05e715843a - Dataset: 1,800 garment-disjoint episodes; 28,800 deformation observations
- Categories: tops, dress, trousers
- Seeds published here: seed 0 only
- Completion: 48/48 trained cells; no failed or non-finite runs
The full category-level results are in comparison/summary.json and
comparison/runs.csv. Verify downloaded artifacts with SHA256SUMS.
- License
- mit
- Created
- Sep 10, 2026
- Updated
- Sep 10, 2026