zengzzy/PatchLocks-data

PatchLocks — data

Data 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. Weights: zengzzy/PatchLocks.

The repository layout is a data root for the code: download it and link it in.

# from the code repository: everything, or subsets (training | testsets | animals | figures)
bash scripts/download_data.sh /path/to/DATA_ROOT
bash scripts/download_data.sh /path/to/DATA_ROOT testsets
# or directly with the Hugging Face CLI (pip install huggingface_hub), then link:
hf download zengzzy/PatchLocks-data --repo-type dataset --local-dir /path/to/DATA_ROOT
bash pipeline/00_link_data.sh /path/to/DATA_ROOT

Path	Size	Contents	Used for
patchlocks/datasets/v11_longfur/renders_mv/	13 GB	1,600 synthetic fur patches (nine coat archetypes), each with 3D strands (patch.npz), 4 calibrated 256 px renders, codec latents, field targets and pose features	training; its 80-patch validation subset is Table 1
patchlocks/experiments/rebuttal_2027/testB_*	2.3 GB	frozen in-distribution test set, n=160	Table 2 (ID), supplementary ceiling table
patchlocks/experiments/rebuttal_2027/ood_*	1.1 GB	frozen extrapolation test set, n=80, four beyond-range coats	Table 2 (EXT)
patchlocks/experiments/rebuttal_2027/grid5_B/	91 MB	per-predictor patch renders	fig_baselines_qual
patchlocks/datasets/v16/v16_visual_gate_agx/	67 MB	patch-level input / ground truth / prediction renders	supplementary visual-gate figures
v2_results/refine_v16_fluffy/	5 GB	the four reconstructed animals of the paper: per-step outputs (diffusion predictions, character, swatch, fitted parameters) and the final grooms (<animal>/grow/surfpack_v2fur.npz, 1.1–1.2M strands × 32 points), plus the renders used by the figures	paper figures, tab:animals
v2_results/refine_v16_4animals/	5 GB	an earlier deployment of the same four animals	supplementary seam table
v2_results/refine_v16_paper_smooth/global4_champagne/	3 MB	renders of the smooth-loft panda variant	supplementary turntables
v2_results/paper_cover_patch/beagle_p855/	12 MB	one predicted patch and its renders	supplementary patch-result figure
patchlocks/deploy/nf_official_render/render_*_ours*/	30 MB	our side of the NeuralFur comparison renders	Fig. 3, supplementary comparison

The synthetic patches use seeds 210000+ (training), 610000+ (in-distribution test) and 810000+ (extrapolation); the test sets were generated once and frozen (*_FROZEN_manifest.txt in the code repository). Generation and evaluation protocols: docs/DATA.md and docs/EVALUATION.md in the code repository.

Not included. The real-animal inputs (multi-view captures, masks, orientation maps, furless meshes and the surface patch packs preprocessed from them) come from NeuralFur (CC BY-NC-SA 4.0) and ARTEMIS; obtain them from those projects. Files that embed their imagery or reconstructions were removed from this release: photo-overlay diagnostics of the orientation fit, photo-projected albedo, capture photos, renders of NeuralFur's grooms, and the NeuS / GenZoo baseline renders. Figures that need those inputs (Fig. 3 and the baseline grid) can only be rebuilt with the third-party data. The reconstructed grooms and renders of the four real animals (v2_results/, nf_official_render/) were fitted to those captures and are shared under CC BY-NC-SA 4.0 with attribution to NeuralFur and ARTEMIS. The license of the synthetic data will be stated with the code release.