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README.md

@@ -0,0 +1,138 @@
+---
+license: cc-by-nc-4.0
+library_name: pytorch
+pipeline_tag: image-to-3d
+base_model: veichta/zipsplat
+base_model_relation: finetune
+tags:
+  - applied-intuition
+  - 3d-reconstruction
+  - gaussian-splatting
+  - novel-view-synthesis
+  - streaming
+  - feed-forward
+  - arxiv:2610.08978
+paper: 2610.08978
+---
+
+<p align="center">
+  <img src="https://huggingface.co/AppliedIntuitionResearch/S2Tok/resolve/main/assets/icon.svg" width="120"/>
+</p>
+
+# S2Tok: Streaming 3D Gaussian Reconstruction with Persistent Spatial Tokens
+
+<p align="center">
+  <a href="https://arxiv.org/abs/2610.08978"><img src="https://img.shields.io/badge/arXiv-2610.08978-b31b1b.svg" alt="arXiv"></a>
+  <a href="https://s2tok.github.io/"><img src="https://img.shields.io/badge/Project-Website-blue" alt="Project Page"></a>
+  <a href="https://github.com/Applied-Intuition-Open-Source/S2Tok"><img src="https://img.shields.io/badge/GitHub-Code-black?logo=github" alt="Code"></a>
+  <a href="https://creativecommons.org/licenses/by-nc/4.0/"><img src="https://img.shields.io/badge/License-CC%20BY--NC%204.0-lightgrey" alt="License"></a>
+</p>
+
+S2Tok reconstructs a scene as 3D Gaussians from an uncalibrated image stream, one frame at a time.
+It keeps a persistent, size-adaptive set of spatial tokens as its scene state: each new frame
+updates the existing tokens, and a learned admission module adds new tokens only for content that
+is not yet represented. A hierarchical decoder turns the tokens into non-pixel-aligned Gaussians,
+so the scene can be rendered from new views at any point of the stream without keeping past frames.
+
+> ⚠️ **Research release.** This model is released for research purposes under the terms below. It is not a production system and does not include any Applied Intuition proprietary data, product code, or production checkpoints.
+
+## Model Details
+
+| | |
+|---|---|
+| **Developed by** | Applied Intuition — AI Research |
+| **Model type** | Streaming feed-forward transformer (DA3-Giant ViT-G/14 backbone) with a persistent token memory, a learned admission module and a 3D Gaussian decoder |
+| **Paper** | [S2Tok: Streaming 3D Gaussian Reconstruction with Persistent Spatial Tokens](https://arxiv.org/abs/2610.08978) |
+| **Code** | https://github.com/Applied-Intuition-Open-Source/S2Tok |
+| **Project page** | https://s2tok.github.io/ |
+| **Initialized from** | [ZipSplat](https://huggingface.co/veichta/zipsplat) (CC BY-NC 4.0), itself initialized from [DA3-Giant](https://huggingface.co/depth-anything/DA3-GIANT) (CC BY-NC 4.0) |
+| **Training data** | [DL3DV-10K](https://github.com/DL3DV-10K/Dataset) and [RealEstate10K](https://google.github.io/realestate10k/) |
+| **License (weights)** | [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) — see [WEIGHTS_LICENSE.md](WEIGHTS_LICENSE.md) |
+| **Contact** | GitHub Issues on the code repo |
+
+## Checkpoints
+
+| Checkpoint | Description | Size | Link |
+|---|---|---|---|
+| `s2tok_252.pt` | 1.72B params. 252x252 input, trained on 12–24 frame sequences from RealEstate10K and DL3DV-10K. | 6.87 GB | [s2tok_252.pt](s2tok_252.pt) |
+
+The file holds `model` (inference weights) and `config` (architecture settings), and loads with
+`torch.load(..., weights_only=True)`. SHA-256 checksums are in [SHA256SUMS](SHA256SUMS).
+
+## Intended Use & Limitations
+
+**Intended use:** research on streaming and feed-forward 3D reconstruction and novel-view
+synthesis.
+
+**Out of scope / limitations:**
+
+- Frames are center-cropped and resized to 252x252. Gaussians are expressed in the camera frame of
+  the first image, up to a global scale.
+- The memory holds at most 4,000 tokens (128k Gaussians); once it is full, no new content is
+  admitted.
+- Trained on 12–24 frame sequences of static scenes and evaluated at 12 and 50 views; retention
+  over much longer streams is not established.
+
+**Commercial use is not permitted** under CC BY-NC 4.0. The weights are also subject to the terms
+of their upstream model and datasets (see [License](#license)).
+
+## How to Use
+
+```python
+from huggingface_hub import hf_hub_download
+from s2tok import load_model
+
+path = hf_hub_download("AppliedIntuitionResearch/S2Tok", "s2tok_252.pt")
+model = load_model(path, device="cuda")
+
+memory = model.init_memory()
+for image in frames:  # [3, 252, 252] RGB tensors in [0, 1]
+    out, memory = model.step(image.cuda(), memory)
+out.gaussians.save_ply("scene.ply")  # Gaussians after the last frame (3DGS PLY)
+```
+
+Streaming demo with an interactive viewer:
+
+```bash
+python demo.py --model_path s2tok_252.pt --seq_path path/to/video.mp4 --output_dir outputs/my_scene
+```
+
+Full installation and usage instructions: see the [GitHub repository](https://github.com/Applied-Intuition-Open-Source/S2Tok).
+
+## License
+
+The weights are released under [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/),
+**non-commercial use only** ([WEIGHTS_LICENSE.md](WEIGHTS_LICENSE.md), full text in
+[LICENSE](LICENSE)). The non-commercial restriction is also required by their upstream sources:
+
+- fine-tuned from the released [ZipSplat](https://github.com/cvg/ZipSplat) checkpoint
+  ([CC BY-NC 4.0](https://huggingface.co/veichta/zipsplat)), which is initialized from
+  [DA3-Giant](https://huggingface.co/depth-anything/DA3-GIANT) (CC BY-NC 4.0); depth labels for
+  training were produced with [DA3-Nested-Giant-Large](https://huggingface.co/depth-anything/DA3NESTED-GIANT-LARGE)
+  (CC BY-NC 4.0);
+- trained on [DL3DV-10K](https://github.com/DL3DV-10K/Dataset) (DL3DV-10K Terms of Use and
+  CC BY-NC 4.0: non-commercial research and education only) and
+  [RealEstate10K](https://google.github.io/realestate10k/) (Google; frames from YouTube videos).
+
+Use of the weights must also comply with the terms of these models and datasets. No training
+data is included in this repository.
+
+## Citation
+
+```bibtex
+@article{li2026s2tok,
+  title   = {S2Tok: Streaming 3D Gaussian Reconstruction with Persistent Spatial Tokens},
+  author  = {Li, Fang and Yenphraphai, Jiraphon and Herau, Quentin and Meng, Depu and Hu, Yihan and Xu, Tianshuo and Ahuja, Narendra and Zhan, Wei},
+  journal = {arXiv preprint arXiv:2610.08978},
+  year    = {2026}
+}
+```
+
+## Acknowledgments
+
+This model builds on [ZipSplat](https://github.com/cvg/ZipSplat) (code Apache-2.0, weights
+CC BY-NC 4.0), [Depth Anything 3](https://github.com/ByteDance-Seed/Depth-Anything-3) and
+[DINOv2](https://github.com/facebookresearch/dinov2) (Apache-2.0). It is trained on
+[DL3DV-10K](https://github.com/DL3DV-10K/Dataset) and
+[RealEstate10K](https://google.github.io/realestate10k/). We thank the authors for making their
+work available.

SHA256SUMS

@@ -0,0 +1 @@
+e1a63805101a187297566ff25fb6a2f7730cae2c830eac556144a20d4934db7b  s2tok_252.pt

WEIGHTS_LICENSE.md

@@ -0,0 +1,25 @@
+# S2Tok model weights: license and terms
+
+The pretrained S2Tok weights (`s2tok_252.pt`, on [Hugging Face](https://huggingface.co/AppliedIntuitionResearch/S2Tok)) are licensed under the
+[Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/),
+the same license as the code; the full text is in [LICENSE](LICENSE).
+**Non-commercial use only.**
+
+The non-commercial restriction is also required by the weights' upstream dependencies:
+
+- they are fine-tuned from the released [ZipSplat](https://github.com/cvg/ZipSplat) checkpoint
+  (CC BY-NC 4.0),
+- which is initialized from [DA3-Giant](https://huggingface.co/depth-anything/DA3-GIANT)
+  (CC BY-NC 4.0),
+- depth labels for training were produced with
+  [DA3-Nested-Giant-Large](https://huggingface.co/depth-anything/DA3NESTED-GIANT-LARGE) (CC BY-NC 4.0),
+- and they are trained on [DL3DV-10K](https://github.com/DL3DV-10K/Dataset) (non-commercial terms
+  of use) and [RealEstate10K](https://google.github.io/realestate10k/).
+
+Use of the weights must also comply with the terms of these upstream models and datasets.
+
+**Attribution.** When sharing the weights or anything derived from them, credit "S2Tok, The S2Tok
+Authors", link to this repository and to the license, and indicate whether changes were made.
+
+**No warranty.** The weights are provided "as is", without warranties or conditions of any kind;
+see Section 5 of the license.

s2tok_252.pt

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