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PartTrellis corpus — 4-source part-labelled training data
The full training and test corpus of PartTrellis: 19,132 training objects
(lists/tri_train_dual.txt) and 1,000 held-out test objects
(lists/test1000.txt), drawn uniformly from four part-labelled sources:
Articraft, PartNeXt, Fusion360, and TRELLIS-500K assets. Includes the original
textured meshes, the rendered conditioning views, and all precomputed training
targets (O-Voxel dual volumes and latents), so both training and evaluation can
run without re-doing preprocessing.
Layout
shards/assets_000.tar … # ~1,000 objects each (train ∪ test)
<source>/<oid>/mesh.glb original textured mesh
<source>/<oid>/face_ids.npy per-face part labels
<source>/<oid>/meta.json
cond/<source>/<oid>/renders_cond_pbr/ PBR conditioning views + transforms.json
latents/latents_000.tar … # 5,000 training objects each
<source>/<oid>/A.vxz B.vxz packed dual O-Voxel volumes (Stage-1 target)
<source>/<oid>/tri_ss_latent.npz sparse-structure latent
<source>/<oid>/tri_slat.npz SLat latent (legacy fitted-VAE space)
<source>/<oid>/tri_meta.json bipartite contraction metadata
latents/origslat.tar # SLat latents in the ORIGINAL TRELLIS.2 VAE
# space — the Stage-2 target of the released model
eval/gt_cache.tar # cached GT part point samples for the evaluator
lists/ # train/test/per-source curation lists + recipes
Note on Stage-2 latents: the released PartTrellis Stage-2
(stage2_slat_orig_100k.pt) trains on latents/origslat.tar (original
TRELLIS.2 VAE space). tri_slat.npz inside the latent shards is the legacy
fitted-VAE space kept for reproducing older ablations; do not mix the two.
Preprocessing provenance
Meshes are voxelized as-is after rigid normalization only (centering, unit
cube, Z-up) — O-Voxel is a field-free surface proxy, so no watertighting,
remeshing, or cleaning is applied. Part contact graphs are contracted until
bipartite and packed into two volumes (A.vxz/B.vxz). All preprocessing
scripts ship in the code tarball of
https://huggingface.co/AuroraRyan/PartTrellis (attraction/data/).
Licensing
The packaging and lists are released for research use; every asset remains under the license of its original distribution.
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