---
license: cc-by-4.0
---
# Dataset Card for MPI3D-realistic

## Dataset Description

The **MPI3D-realistic dataset** is a **photorealistic synthetic image dataset** designed for benchmarking algorithms in **disentangled representation learning** and **unsupervised representation learning**. It is part of the broader MPI3D dataset suite, which also includes [synthetic toy](https://huggingface.co/datasets/randall-lab/mpi3d-toy), [real-world](https://huggingface.co/datasets/randall-lab/mpi3d-real) and [complex real-world](https://huggingface.co/datasets/randall-lab/mpi3d-complex) variants.

The **realistic version** was rendered using a **physically-based photorealistic renderer** applied to **CAD models** of physical 3D objects. The rendering simulates realistic lighting, materials, and camera effects to closely match the real-world recordings of the MPI3D-real dataset. This enables researchers to systematically study **sim-to-real transfer** and assess how well models trained on high-fidelity synthetic images generalize to real-world data.

All images depict **3D objects** under **controlled variations of 7 known factors**:

- Object color (6 values)
- Object shape (6 values)
- Object size (2 values)
- Camera height (3 values)
- Background color (3 values)
- Robotic arm horizontal axis (40 values)
- Robotic arm vertical axis (40 values)

The dataset contains **1,036,800 images** at a resolution of **64×64 pixels** (downsampled from the original resolution for benchmarking, as commonly used in the literature). All factors are **identical** to those used in the toy and real versions of MPI3D, enabling direct comparisons between different domains.
![Dataset Visualization](https://huggingface.co/datasets/randall-lab/mpi3d-realistic/resolve/main/realistic1.gif)

## Dataset Source
- **Homepage**: [https://github.com/rr-learning/disentanglement_dataset](https://github.com/rr-learning/disentanglement_dataset)
- **License**: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/)
- **Paper**: Muhammad Waleed Gondal et al. _On the Transfer of Inductive Bias from Simulation to the Real World: A New Disentanglement Dataset_. NeurIPS 2019.

## Dataset Structure
|Factors|Possible Values|
|---|---|
|object_color|white=0, green=1, red=2, blue=3, brown=4, olive=5|
|object_shape|cone=0, cube=1, cylinder=2, hexagonal=3, pyramid=4, sphere=5|
|object_size|small=0, large=1|
|camera_height|top=0, center=1, bottom=2|
|background_color|purple=0, sea green=1, salmon=2|
|horizontal_axis (DOF1)|0,...,39|
|vertical_axis (DOF2)|0,...,39|

Each image corresponds to a unique combination of these 7 factors. The images are stored in a **row-major order** (fastest-changing factor is `vertical_axis`, slowest-changing factor is `object_color`).

### Why no train/test split?
The MPI3D-realistic dataset does not provide an official train/test split. It is designed for **representation learning research**, where the goal is to learn disentangled and interpretable latent factors. Since the dataset is a complete Cartesian product of all factor combinations, models typically require access to the full dataset to explore factor-wise variations.

## Example Usage
Below is a quick example of how to load this dataset via the Hugging Face Datasets library:
```python
from datasets import load_dataset

# Load the dataset
dataset = load_dataset("randall-lab/mpi3d-realistic", split="train", trust_remote_code=True)
# Access a sample from the dataset
example = dataset[0]
image = example["image"]
label = example["label"]  # [object_color: 0, object_shape: 0, object_size: 0, camera_height: 0, background_color: 0, horizontal_axis: 0, vertical_axis: 0]
color = example["color"] # 0 
shape = example["shape"] # 0
size = example["size"] # 0
height = example["height"] # 0
background = example["background"] # 0 
dof1 = example["dof1"] # 0 
dof2 = example["dof2"] # 0

image.show()  # Display the image
print(f"Label (factors): {label}")
```
If you are using colab, you should update datasets to avoid errors
```
pip install -U datasets
```
## Citation
```
@article{gondal2019transfer,
  title={On the transfer of inductive bias from simulation to the real world: a new disentanglement dataset},
  author={Gondal, Muhammad Waleed and Wuthrich, Manuel and Miladinovic, Djordje and Locatello, Francesco and Breidt, Martin and Volchkov, Valentin and Akpo, Joel and Bachem, Olivier and Sch{\"o}lkopf, Bernhard and Bauer, Stefan},
  journal={Advances in Neural Information Processing Systems},
  volume={32},
  year={2019}
}
```