Datasets:

nikoloside
/

break4models

Break4Models is a small-scale 4-shape dataset for deepfracture paper. It contains simulation data of various 3D objects undergoing impact and breaking fracture pattern, designed for training and evaluating neural networks that can predict object fracture patterns for specific one target shape.

Dataset Structure

The dataset is organized by object categories:

_out_base/ - Base object simulations (277 samples)
_out_pot/ - Pot object simulations (433 samples)
_out_squirrel/ - Squirrel object simulations (529 samples)
_out_bunny/ - Bunny object simulations (500 samples)

Each category contains:

meta.json - Metadata with simulation statistics
obj/ - 3D mesh files (.obj format) for each simulation step
info/ - Collision information files
impact/ - Detailed impact data in JSON format
initial_cond/ - Initial conditions for simulations
nii/ - NIfTI format data (if applicable)
gif/ - Animation files showing breaking process

Data Collection

Source Data

The dataset was created by FractureRB.

Annotations

Impact Data: JSON format containing collision information
- collElems: Collision element IDs
- collPoints: Collision point coordinates [x, y, z]
- collDirections: Collision direction vectors
- collVels: Collision velocities
- collImpulse: Collision impulse values
- collForce: Collision force magnitudes
Info Files: Simple format directory/,simulation-index-number

Personal and Sensitive Information

This dataset contains no personal or sensitive information as it consists entirely of synthetic physics simulation data.

Dataset Statistics

Dataset Size

Total Objects: 4 target shapes (base, pot, squirrel, bunny)
Total Simulations: Varies by category
- Base: 277 simulations
- Pot: 433 simulations
- Squirrel: 529 simulations
- Bunny: 500 simulations

Data Fields

target_shape: The target object shape name
max_gssdf_val: Maximum GS-SDF (Geometrically-Segmented Signed Distance Field) value
avg_max_gssdf_val: Average maximum GS-SDF value
min_gssdf_val: Minimum GS-SDF value
avg_min_gssdf_val: Average minimum GS-SDF value
start_time: Simulation start time
stop_time: Simulation end time
total_valid: Total number of valid simulation times

Uses

Direct Use

This dataset is designed for:

Training neural networks for physics-based collision-conditional brittle fracture
Evaluating fracture prediction models for deepfracture paper
Research in computational physics and computer graphics
Demo for real-time brittle fracture simulation systems

Out-of-Scope Use

Medical applications
Safety-critical systems
High-precision engineering simulations without additional validation

Bias, Risks, and Limitations

Bias

The dataset is generated from synthetic simulations and may not perfectly represent real-world physics. The objects and materials are idealized and may not capture all real-world complexities.

Risks

Models trained on this data may not generalize to real-world scenarios
Performance may degrade with objects significantly different from training data
No guarantees for physical accuracy in safety-critical applications

Limitations

Limited to the specific object categories in the dataset
Synthetic data may not capture all real-world physics phenomena
Requires significant computational resources for processing

Training and Evaluation

Training Data

The dataset is split into training and test sets:

Training set: 200 samples
Test set: 50 samples

This split is used for model development and evaluation.

Evaluation Data

Evaluation metrics include:

Geometric accuracy
Physics consistency
Computational efficiency

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)

Data Card Contact

https://github.com/nikoloside/TEBP