PatchLocks — model weights

Weights for PatchLocks, which reconstructs animal fur as individual strands from multi-view images with one learned patch prior. Code, documentation and usage guide (docs/USAGE.md): github.com/CCdydy/PatchLocks. Data: zengzzy/PatchLocks-data.

Files keep the paths the code expects. From a clone of the code repository:

bash scripts/download_weights.sh            # curl + sha256 check, no extra dependencies
# or with the Hugging Face CLI (pip install huggingface_hub):
hf download zengzzy/PatchLocks --local-dir . --exclude "README.md" ".gitattributes"

File	Size	Used for
patchlocks/datasets/v16/e2a_cw15/v14_best.pt	148 MB	paper diffusion model: multi-view EDM over 64-channel strand latents with clump-channel weighting (all whole-animal results, Table 1 "ours")
patchlocks/datasets/v16/e2a_cw15/b3_fuser.pt	8.6 MB	multi-view fuser paired with the paper model
patchlocks/models/fur_strand_codec_v6upright.pt	6.8 MB	strand codec (z64 ↔ strand), fine-tuned from DiffLocks
patchlocks/v2_locked/V10_PATCH_STABLE/stage3_model.pt, b3_fuser.pt	72 MB	single-view baseline (Table 1 "1-view")
patchlocks/datasets/v14/e1_full_sm80/v14_best.pt, b3_fuser.pt	157 MB	recipe ablation without clump-channel weighting (supplementary recipe table)
patchlocks/experiments/rebuttal_2027/det_z64/det_z64_best.pt	152 MB	deterministic-latent baseline (Table 2)
patchlocks/experiments/rebuttal_2027/direct7/direct7_best.pt	6.5 MB	scalar-regression baseline (Table 2)

Training: 1,600 synthetic fur patches × 4 views (nine coat archetypes), seed 0, ~30 min on one NVIDIA RTX PRO 6000 (pipeline/02_train_diffusion.sh). Re-running the released checkpoint reproduces the paper's diffusion predictions bit for bit and every paper table; see docs/REPRODUCIBILITY.md in the code repository.

The strand codec architecture and its initial weights come from DiffLocks. The conditioning backbone is DINOv2, loaded through torch.hub.