initial commit

.gitattributes

@@ -0,0 +1,4 @@
+*.gif filter=lfs diff=lfs merge=lfs -text
+*.nii filter=lfs diff=lfs merge=lfs -text
+*.txt filter=lfs diff=lfs merge=lfs -text
+*.jpg filter=lfs diff=lfs merge=lfs -text

LICENSE

@@ -0,0 +1,26 @@
+Public Domain Dedication and License (PDDL)
+
+This is free and unencumbered software released into the public domain.
+
+Anyone is free to copy, modify, publish, use, compile, sell, or
+distribute this software, either in source code form or as a compiled
+binary, for any purpose, commercial or non-commercial, and by any
+means.
+
+In jurisdictions that recognize copyright laws, the author or authors
+of this software dedicate any and all copyright interest in the
+software to the public domain. We make this dedication for the benefit
+of the public at large and to the detriment of our heirs and
+successors. We intend this dedication to be an overt act of
+relinquishment in perpetuity of all present and future rights to this
+software under copyright law.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
+IN NO EVENT SHALL THE AUTHORS BE LIABLE FOR ANY CLAIM, DAMAGES OR
+OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE,
+ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+OTHER DEALINGS IN THE SOFTWARE.
+
+For more information, please refer to <http://unlicense.org/>

README.md

@@ -0,0 +1,95 @@
+# Break4Models Dataset
+
+![Teaser](./teaser.jpg)
+
+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. It was created by [FractureRB](https://github.com/david-hahn/FractureRB).
+
+📖 **For more details, please visit:**
+- [GitHub Repository](https://github.com/nikoloside/TEBP)
+- [Project Page](https://nikoloside.graphics/deepfracture/)
+
+## Overview
+
+This dataset contains simulation data of 4-shape 3D objects undergoing impact and breaking fracture pattern. Each target shape includes multiple simulation runs with different impact conditions, providing a small-scale dataset for learning relationship between impact collision condition and physics-based fracture patterns.
+
+## Dataset Structure
+
+```
+break4models/
+├── _out_base/          # Base object simulations
+├── _out_pot/           # Pot object simulations  
+├── _out_squirrel/      # Squirrel object simulations
+├── _out_bunny/         # Bunny object simulations
+└── README.md           # This file
+```
+
+Each object category directory 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 Format
+
+### Meta Information (`meta.json`)
+```json
+{
+  "target_shape": "base",
+  "max_gssdf_val": 0.128913,
+  "avg_max_gssdf_val": 0.127481,
+  "min_gssdf_val": -1.045852,
+  "avg_min_gssdf_val": -1.045852,
+  "start_time": "2024-03-30T08:58:40",
+  "stop_time": "2024-03-30T10:40:07",
+  "total_valid": 277
+}
+```
+
+### Impact Data (`impact/*.txt`)
+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 (`info/*.txt`)
+Simple format: `directory/,frame_number`
+
+## Usage
+
+This dataset is designed for:
+- Training neural networks for physics-based fracture pattern
+- Evaluating fracture prediction models
+- Research in computational physics and computer graphics
+- Development of real-time simulation systems
+
+## Citation
+
+If you use this dataset in your research, please cite:
+
+```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}
+      }
+```
+
+## License
+
+This dataset is licensed under the **Public Domain Dedication and License (PDDL)**.
+
+## Contact
+
+For questions or issues, please open an issue on the Hugging Face dataset page.

data_card.md

@@ -0,0 +1,155 @@
+# Data Card for Break4Models
+
+## Dataset Description
+
+- **Dataset Name**: Break4Models
+- **Dataset Type**: Physics simulation data
+- **Language**: English
+- **License**: PDDL (Public Domain Dedication and License)
+- **Created by**: [FractureRB](https://github.com/david-hahn/FractureRB)
+
+### Dataset Sources
+
+- **Repository**: [GitHub Repository](https://github.com/nikoloside/TEBP)
+- **Paper**: [DeepFracture: A Generative Approach for Predicting Brittle Fractures with Neural Discrete Representation Learning](https://onlinelibrary.wiley.com/doi/abs/10.1111/cgf.70002)
+- **Project Page**: [Project Page](https://nikoloside.graphics/deepfracture/)
+
+## Dataset Overview
+
+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](https://github.com/david-hahn/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
+
+```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

dataset_info.json

@@ -0,0 +1,68 @@
+{
+  "dataset_name": "break4models",
+  "description": "A 4-shape small-scale dataset for physics-based 3D object breaking simulation and reconstruction",
+  "homepage": "https://huggingface.co/datasets/nikoloside/break4models",
+  "license": "PDDL",
+  "features": {
+    "target_shape": {
+      "dtype": "string",
+      "description": "The target object shape name"
+    },
+    "max_gssdf_val": {
+      "dtype": "float32",
+      "description": "Maximum GS-SDF (Geometrically-Segmented Signed Distance Field) value"
+    },
+    "avg_max_gssdf_val": {
+      "dtype": "float32", 
+      "description": "Average maximum GS-SDF value"
+    },
+    "min_gssdf_val": {
+      "dtype": "float32",
+      "description": "Minimum GS-SDF value"
+    },
+    "avg_min_gssdf_val": {
+      "dtype": "float32", 
+      "description": "Average minimum GSSDF value"
+    },
+    "start_time": {
+      "dtype": "string",
+      "description": "Simulation start timestamp"
+    },
+    "stop_time": {
+      "dtype": "string",
+      "description": "Simulation end timestamp"
+    },
+    "total_valid": {
+      "dtype": "int32",
+      "description": "Total number of valid simulation Times"
+    }
+  },
+  "config_name": "default",
+  "version": {
+    "version_str": "1.0.0",
+    "description": "Initial release",
+    "major": 1,
+    "minor": 0,
+    "patch": 0
+  },
+  "splits": {
+    "train": {
+      "name": "train",
+      "num_bytes": 0,
+      "num_examples": 200,
+      "shard_lengths": []
+    },
+    "test": {
+      "name": "test", 
+      "num_bytes": 0,
+      "num_examples": 50,
+      "shard_lengths": []
+    }
+  },
+  "download_checksums": {},
+  "download_size": 0,
+  "post_processed": null,
+  "supervised_keys": null,
+  "task_templates": [],
+  "builder_name": "json"
+}