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+ "bytes": 50352,
+ "sha256": "265ac05a48b41523cd44a49167f4857cab9c0acc9298ad7dd4d204b135573218"
+ },
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+ "sha256": "09e0f564364c156118e9fc1ea596a83f34f05d4d0058d18b497160bfe50ab8e8"
+ }
+ },
+ "scrubbed": [
+ "ckpt/image_model/model.safetensors render_root: workspace path -> 'data/renders'",
+ "ckpt/image_model/model.safetensors: dropped keys ['args.data_tag']",
+ "ckpt/card_vae/model.safetensors: dropped keys ['args.arch', 'args.data_tag']",
+ "data/avatar/body.json: dropped keys ['blender_meta', 'made_by', 'vrm_meta_note'], set ['dir', 'key', 'vrm', 'vrm_meta.title']"
+ ]
+}
\ No newline at end of file
diff --git a/README.md b/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..a0e918421056861310237d2f26db54ab15a025c8
--- /dev/null
+++ b/README.md
@@ -0,0 +1,115 @@
+---
+license: apache-2.0
+library_name: pytorch
+pipeline_tag: image-to-3d
+tags:
+- 3d
+- anime
+- hair
+- hair-cards
+- vrm
+- diffusion
+- arxiv:2610.40599
+---
+
+HairDeck: Animation-Ready Stylized Hair Cards
from a Single Image
+
+Yuze He1,* Yixiang Wei1,* Wang Zhao1 Chuhao Chen2 Yanning Zhou3,† Yong-Jin Liu1,†
+1Tsinghua University 2Carnegie Mellon University 3The Chinese University of Hong Kong
+*Equal contribution. †Corresponding authors.
+
+
+
+ 
+ 
+ 
+ 
+
+
+**HairDeck turns a single character image into stylized hair cards with per-card UVs and colors, and the spring-bone chains that animate them.** Generate hair meshes as GLB files, then attach the hair to the provided avatar body and export a VRM 0.x avatar.
+
+
+
+## 🚀 Try HairDeck
+
+Start with the [online demo](https://huggingface.co/spaces/hyz317/HairDeck), or run the models locally with the [HairDeck code](https://github.com/hyz317/HairDeck).
+
+For local use, follow the [installation instructions](https://github.com/hyz317/HairDeck#installation) for Linux, Python 3.12 and a CUDA GPU. Accept the SAM 3 and DINOv3 licences and log in as described in [model setup](https://github.com/hyz317/HairDeck#model-weights). Then, from the code repository root:
+
+```bash
+export HAIRDECK_ROOT=/path/to/workspace
+python scripts/setup/download_weights.py
+python scripts/image_to_hair.py character.png --out results/my_character --n 4
+python -m hairdeck.vrm.export results/my_character/character 0
+```
+
+`download_weights.py` downloads the model bundle and supporting assets. Inference writes four variations under `results/my_character/character/`; the export command creates a VRM and head-turn video for seed 0. Video previews require `ffmpeg`. See the [output guide](https://github.com/hyz317/HairDeck#outputs) for the generated files.
+
+## 🧩 Released models
+
+| Model | Role | Weights |
+|---|---|---|
+| **Image DiT** | Generates hair-card tokens conditioned on an image. | `ckpt/image_model/model.safetensors` |
+| **Card VAE** | Decodes each card token's latent into card geometry. | `ckpt/card_vae/model.safetensors` |
+| **Chain DiT** | Generates spring-bone chains conditioned on the card tokens. | `ckpt/chain_model/model.safetensors` |
+| **Color DiT** | Predicts the colors along each card, conditioned on the card tokens and the image. | `ckpt/color_model/model.safetensors` |
+| **Point-cloud DiT** | Generates hair-card tokens conditioned on a point cloud. | `ckpt/pointcloud_model/model.safetensors` |
+
+The image pipeline combines **Image DiT + Card VAE + Color DiT + Chain DiT**. The point-cloud model is used by the point-cloud generation and evaluation path. Both paths also need the supplied normalization statistics; use the downloader above to obtain the matching files together.
+
+DINOv3, SAM 3, BiRefNet-matting and Dora-VAE are fetched from their own repositories when needed. Their weights are not bundled here. The image model uses DINOv3 ViT-L/16 features, and the point-cloud model uses Dora-VAE 1.1 features.
+
+## 📦 Supporting assets
+
+This repository also provides normalization statistics, the canonical head frame, the avatar body used for VRM export, and example images. Evaluation assets are optional and skipped by the default download.
+
+
+Detailed file layout and checkpoint format
+
+| Path | Content |
+|---|---|
+| `ckpt/card_vae/` | Card VAE weights, normalization statistics and latent statistics. |
+| `data/token_stats.json`, `data/token_stats_head.json` | Card-token normalization in the dataset and head frames. |
+| `data/head_frame_train.npz` | Canonical head-frame data. |
+| `data/spring/chain_stats_head.json` | Chain-token normalization. |
+| `data/avatar/` | The provided VRoid Studio template avatar, its original hair and head-frame fit. |
+| `data/demo_examples/` | Example input images for the demo. |
+| `data/test_cards_orig/`, `data/test_cards_orig_dirs.npz` | Original hair-card geometry and width directions for the 100 test cases. |
+| `data/spring/chains_test.jsonl`, `data/spring/chains_test_head.npz` | Test characters' spring-bone chains and their head-frame tokens. |
+| `data/head_params_test.json`, `data/head_frame_test.npz`, `data/cards_test.npz` | Test head fits, head frames and card features. |
+| `ckpt/card_vae/latents_test_head.npy` | Head-frame latents of the test cards for chain-model conditioning. |
+| `config.json` | Released models, supporting files and checkpoint format. |
+| `MANIFEST.json` | File sizes and SHA-256 hashes for the downloadable model/data bundle. |
+
+The DiT checkpoints contain EMA weights; the Card VAE contains its model weights. Training configuration is stored in the safetensors metadata under `hairdeck` and read by `hairdeck.ckpt.load`. Use the HairDeck code to load these checkpoints.
+
+
+
+## 📊 Evaluation
+
+To download the additional test assets and point-cloud encoder:
+
+```bash
+python scripts/setup/download_weights.py --only test_data charm_test_pc dora
+```
+
+Follow the [evaluation guide](https://github.com/hyz317/HairDeck#test-set-evaluation) to run the point-cloud and chain benchmarks. The default tests sample seeds 0, 1 and 2 and save per-sample metrics and their means. Training instructions are in the [code repository](https://github.com/hyz317/HairDeck#training).
+
+## 📝 Citation
+
+```bibtex
+@misc{hairdeck2026,
+ title = {HairDeck: Animation-Ready Stylized Hair Cards from a Single Image},
+ author = {Yuze He and Yixiang Wei and Wang Zhao and Chuhao Chen and Yanning Zhou and Yong-Jin Liu},
+ year = {2026},
+ eprint = {2610.40599},
+ archivePrefix = {arXiv},
+ primaryClass = {cs.CV},
+ doi = {10.48550/arXiv.2610.40599},
+ url = {https://arxiv.org/abs/2610.40599}
+}
+```
+
+## License
+
+The HairDeck checkpoints are released under Apache-2.0.
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+{
+ "name": "HairDeck",
+ "format": "safetensors (state dict; args, step and token dim as JSON in the metadata key hairdeck)",
+ "license": "apache-2.0",
+ "manifest": "MANIFEST.json",
+ "models": {
+ "card_vae": {
+ "checkpoint": "ckpt/card_vae/model.safetensors",
+ "params_million": 8.07
+ },
+ "point_cloud_dit": {
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+ "params_million": 181.3,
+ "sampling": "scripts/test.py pointcloud (the test set: 50 Euler steps, CFG 2, seeds 0, 1, 2 in results/pointcloud/seed, then the evaluation)"
+ },
+ "chain_dit": {
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+ "params_million": 30.5,
+ "sampling": "scripts/test.py chains (the test set on the artists' cards: 50 Euler steps, CFG 2, seeds 0, 1, 2 in results/chains/seed