| # Model Card for DeepFracture | |
| ## Model Description | |
| - **Model type**: 3D Fracture Pattern Prediction via impulse-code conditional VQ-VAE models | |
| - **Language(s)**: Python | |
| - **License**: MIT | |
| - **Finetuned from model**: Custom architecture | |
| ### Model Sources | |
| - **Repository**: https://github.com/nikoloside/TEBP | |
| - **Paper**: https://doi.org/10.1111/cgf.70002 | |
| - **Demo**: [WIP] | |
| ## Uses | |
| ### Direct Use | |
| These models are designed for: | |
| - Computer graphics applications | |
| - Game development | |
| - Virtual reality environments | |
| - Paper code release | |
| ### Out-of-Scope Use | |
| - Medical applications | |
| - Safety-critical systems | |
| - High-precision engineering simulations | |
| ## Bias, Risks, and Limitations | |
| ### Bias | |
| The models are trained on synthetic simulation data and may not generalize well to real-world scenarios with different material properties or environmental conditions. | |
| ### Risks | |
| - Models may produce unrealistic deformations under extreme conditions | |
| - Performance may significantly different from training data | |
| - No guarantees for physical accuracy in safety-critical applications | |
| ### Limitations | |
| - Limited to the specific one target shape and object categories in the training dataset | |
| - Be able to reach real-time inference in network, but need resconstruction within 2-10 seconds | |
| ## Training Details | |
| ### Training Data | |
| - **Dataset**: Break4Model dataset | |
| - **Training samples**: Varies by model (277-433 samples per category) | |
| - **Validation samples**: 20% of training data | |
| - **Data preprocessing**: Normalized impact conditions and geometry of GS-SDF | |
| ### Training Procedure | |
| - **Training regime**: Supervised learning | |
| - **Optimizer**: Adam | |
| - **Learning rate**: 1e-3 - 1e-5 | |
| - **Batch size**: 1 | |
| - **Training epochs**: 1000 | |
| - **Hardware**: NVIDIA RTX 3090 GPU | |
| - **Training time**: ~24 hours per model | |
| ### Training Results | |
| (metrics and performance)[https://doi.org/10.1111/cgf.70002] | |
| ## Evaluation | |
| ### Testing Data | |
| - **Dataset**: Break4Models | |
| - **Metrics**: Geometric accuracy, Fragment size, Inference time, Fragment distribution, Surface normals, MPCD (Multiple-Phase Charmfer Distance) | |
| ### Results | |
| The models achieve high accuracy in predicting fracture patterns while maintaining nearly real-time performance suitable for interactive applications. | |
| ## Environmental Impact | |
| - **Hardware Type**: GPU | |
| - **Hours used**: ~24 hours per model | |
| - **Dataset**: Break4Model dataset | |
| - **Framework**: PyTorch | |
| - **Optimizer**: Adam | |
| - **Loss Function**: L2 Loss | |
| - **Training Time**: ~24 hours per model on NVIDIA RTX 3090 | |
| ## Technical Specifications | |
| ### Model Architecture | |
| - **Encoder**: SIREN Layer | |
| - **Decoder**: CNN | |
| - **Parameters**: ~2M parameters per model | |
| - **Input**: Impact conditions (position, velocity, impulse strength) from Bullet3 | |
| - **Output**: GS-SDF (Geometrically-Segmented Signed Distance Fields) | |
| ### Compute Requirements | |
| - **Training**: NVIDIA RTX 3090 or equivalent | |
| - **Inference**: CPU or GPU | |
| - **Memory**: 8GB RAM minimum | |
| - **Storage**: ~200MB per model | |
| ## Citation | |
| ```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}, | |
| keywords = {animation, brittle fracture, neural networks, physically based animation}, | |
| doi = {https://doi.org/10.1111/cgf.70002}, | |
| url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/cgf.70002}, | |
| eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/cgf.70002} | |
| } | |
| ``` | |
| ## Data Card Authors | |
| Huang Niko(nikoloside), Fang Chaowei(fangsunjian) | |
| ## Data Card Contact | |
| https://github.com/nikoloside/TEBP |