README.md

---
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  - split: validation
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task_categories:
- image-to-video
---

# Block World Dataset

This repository contains the Block World dataset for experiments of **FloWM (Flow Equivariant World Models)**, presented in the paper [Flow Equivariant World Models: Memory for Partially Observed Dynamic Environments](https://huggingface.co/papers/2601.01075).

[**Project Page**](https://flowequivariantworldmodels.github.io/) | [**GitHub Repository**](https://github.com/hlillemark/flowm)

## Dataset Summary

The Block World dataset is a 3D partially observed video world modeling benchmark. It is designed to evaluate how world models handle continuous sensory input and underlying symmetries in environment dynamics. The dataset includes three main configurations:

- **dynamic**: The primary environment used for results in the paper, featuring moving objects.
- **static**: A version of the environment with static external objects.
- **tex**: A textured version of the environment to test visual complexity.

Each configuration contains both `train` and `validation` splits.

## Usage

To use this dataset with the FloWM framework, the authors provide a download script in the associated GitHub repository to handle the extraction and setup of the data. For more details, please refer to the [official code repository](https://github.com/hlillemark/flowm).

## Citation

```bibtex
@misc{lillemark2026flowequivariantworldmodels,
    title={Flow Equivariant World Models: Memory for Partially Observed Dynamic Environments}, 
    author={Hansen Jin Lillemark and Benhao Huang and Fangneng Zhan and Yilun Du and Thomas Anderson Keller},
    year={2026},
    eprint={2601.01075},
    archivePrefix={arXiv},
    primaryClass={cs.LG},
    url={https://arxiv.org/abs/2601.01075}, 
  }
```