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
@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)