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| title: DeepFracture Runtime | |
| emoji: 💥 | |
| colorFrom: gray | |
| colorTo: red | |
| sdk: static | |
| pinned: false | |
| license: mit | |
| models: | |
| - nikoloside/deepfracture | |
| tags: | |
| - fracture | |
| - physics | |
| - simulation | |
| - graphics | |
| - three-js | |
| # DeepFracture — Live Web Runtime | |
| Real-time, in-browser neural brittle-fracture demo: click the object to shoot | |
| a projectile; the impact becomes the paper's 7-D collision embedding, a JS | |
| mirror of the Siren encoder selects the nearest VQ-VAE codebook entry | |
| ([nikoloside/deepfracture](https://huggingface.co/nikoloside/deepfracture)), | |
| and the matching pre-decoded fragment set (decoder + watershed segmentation, | |
| baked offline per codebook entry) swaps into the rigid-body simulation. | |
| Three.js + Rapier. Also live at <https://nikoloside.graphics/deepfracture-live/>. | |
| ## Paper | |
| This demo accompanies: | |
| > **DeepFracture: A Generative Approach for Predicting Brittle Fractures with | |
| > Neural Discrete Representation Learning.** Yuhang Huang, Takashi Kanai. | |
| > *Computer Graphics Forum*, e70002, 2025. | |
| > [DOI: 10.1111/cgf.70002](https://doi.org/10.1111/cgf.70002) | |
| ```bibtex | |
| @article{huang2025deepfracture, | |
| author = {Huang, Yuhang and Kanai, Takashi}, | |
| title = {DeepFracture: A Generative Approach for Predicting Brittle | |
| Fractures with Neural Discrete Representation Learning}, | |
| journal = {Computer Graphics Forum}, | |
| pages = {e70002}, | |
| year = {2025}, | |
| doi = {https://doi.org/10.1111/cgf.70002} | |
| } | |
| ``` | |
| Papers: [DeepFracture (CGF 2025)](https://nikoloside.graphics/deepfracture/) · | |
| [Far-From-Boundary Fields (SMI 2026)](https://nikoloside.graphics/far-from-boundary-fields/) · | |
| Code: [TEBP-DeepFracture](https://github.com/nikoloside/TEBP-DeepFracture) | |