| --- |
| tags: |
| - 3d |
| - fur |
| - hair |
| - diffusion |
| - strands |
| library_name: pytorch |
| --- |
| |
| # 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](https://github.com/CCdydy/PatchLocks/blob/main/docs/USAGE.md)): |
| **[github.com/CCdydy/PatchLocks](https://github.com/CCdydy/PatchLocks)**. |
| Data: [zengzzy/PatchLocks-data](https://huggingface.co/datasets/zengzzy/PatchLocks-data). |
|
|
| Files keep the paths the code expects. From a clone of the code repository: |
|
|
| ```bash |
| 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](https://difflocks.is.tue.mpg.de/). The conditioning backbone is DINOv2, loaded |
| through `torch.hub`. |
|
|
