Update README.md

README.md

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-license: cc-by-nc-sa-4.0
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+---
+license: cc-by-nc-sa-4.0
+language:
+- en
+pipeline_tag: image-to-video
+---
+
+# MYRIAD (Envisioning the Future, One Step at a Time)
+[![Project Page](https://img.shields.io/badge/Project-Page-blue)](https://compvis.github.io/myriad)
+[![Paper](https://img.shields.io/badge/arXiv-paper-b31b1b)](_blank)
+[![OWM-95](https://img.shields.io/badge/HuggingFace-Dataset-yellow)](https://huggingface.co/datasets/CompVis/owm-95)
+
+## Paper and Abstract
+
+The MYRIAD (Motion hYpothesis Reasoning via Iterative Autoregressive Diffusion) model was presented in the paper [Envisioning the Future, One Step at a Time](_blank).
+
+From a single image, MYRIAD predicts distributions over sparse point trajectories autoregressively. This allows to predict consistent futures in open-set environments and plan actions by exploring a large number of counterfacual interactions.
+
+## Project Page and Code
+
+- **Project Page**: https://compvis.github.io/myriad
+- **GitHub Repository**: https://github.com/CompVis/flow-poke-transformer
+
+## Usage
+
+For programmatic use, the simplest way to use MYRIAD is via `torch.hub`:
+
+```python
+myriad_openset = torch.hub.load("CompVis/myriad", "myriad_openset")
+
+myriad_billiard = torch.hub.load("CompVis/myriad", "myriad_billiard")
+```
+
+If you wish to integrate MYRIAD in your own codebase, you can copy `model.py` and `dinov3.py` from the [GitHub repository](https://github.com/CompVis/flow-poke-transformer).
+The `MyriadStepByStep` class contains a `predict_simulate` method for unrolling trajectories and a low-level `forward` method to predict distributions for previously observed trajectories.
+
+## Citation
+
+If you find our model or code useful, please cite our paper:
+
+```bibtex
+@inproceedings{baumann2026envisioning,
+  title={Envisioning the Future, One Step at a Time},
+  author={Baumann, Stefan Andreas and Wiese, Jannik and Martorella, Tommaso and Kalayeh, Mahdi M. and Ommer, Bjorn},
+  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
+  year={2026}
+}
+```
+