Improve dataset card and add paper metadata

Hi! I'm Niels from the community science team at Hugging Face. I noticed this dataset is part of the "Flow Equivariant World Models" paper. I've updated the dataset card to include links to the paper, project page, and code repository, as well as adding the `image-to-video` task category and a summary of the available dataset configurations to help researchers find and use your work more easily.

README.md

@@ -112,6 +112,40 @@ configs:
     path: tex/train-*
   - split: validation
     path: tex/validation-*
+task_categories:
+- image-to-video
 ---
 
-Block World dataset for experiments of FloWM: https://huggingface.co/papers/2601.01075.
+# 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}, 
+  }
+```