Improve model card

README.md

@@ -1,41 +1,43 @@
 ---
-license: cc-by-nc-sa-4.0
 language:
 - en
-pipeline_tag: image-to-video
+license: cc-by-nc-sa-4.0
+pipeline_tag: other
 ---
 
 # 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)](https://arxiv.org/abs/2604.09527)
+[![GitHub](https://img.shields.io/badge/GitHub-Code-black)](https://github.com/CompVis/flow-poke-transformer)
 [![OWM-95](https://img.shields.io/badge/HuggingFace-Dataset-yellow)](https://huggingface.co/datasets/CompVis/owm-95)
+[![Paper](https://img.shields.io/badge/Huggingface-Papers-yellow)](https://huggingface.co/papers/2604.09527)
 
-## 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).
+MYRIAD (Motion hYpothesis Reasoning via Iterative Autoregressive Diffusion) is an autoregressive diffusion model that predicts open-set future scene dynamics as step-wise inference over sparse point trajectories. Starting from a single image, it can efficiently explore thousands of plausible future outcomes, maintaining physical plausibility.
 
-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.
+## Paper and Abstract
 
-## Project Page and Code
+The MYRIAD model was presented in the paper [Envisioning the Future, One Step at a Time](https://arxiv.org/abs/2604.09527).
 
-- **Project Page**: https://compvis.github.io/myriad
-- **GitHub Repository**: https://github.com/CompVis/flow-poke-transformer
+From a single image, MYRIAD predicts distributions over sparse point trajectories autoregressively. This allows the model to predict consistent futures in open-set environments and plan actions by exploring a large number of counterfactual interactions.
 
 ![MYRIAD predicts distributions over potential motion auto-regressively](https://compvis.github.io/myriad/static/images/paper-svg/teaser-qualitative.svg)
 *From a single image, our model envisions diverse, physically consistent futures by predicting sparse point trajectories step by step.*
 
-
 ![Sampling from MYRIAD enables planning-by-search](https://compvis.github.io/myriad/static/images/paper-svg/teaser-billiards.svg)
 *Its efficiency enables exploring thousands of counterfactual rollouts directly in motion space - here illustrated for billiards planning, where candidate shots are evaluated by simulating many possible outcomes.*
 
-
 ## Usage
 
 For programmatic use, the simplest way to use MYRIAD is via `torch.hub`:
 
 ```python
+import torch
+
+# Load the open-set model
 myriad_openset = torch.hub.load("CompVis/myriad", "myriad_openset")
 
+# Load the billiard-specific model
 myriad_billiard = torch.hub.load("CompVis/myriad", "myriad_billiard")
 ```
 
@@ -53,5 +55,4 @@ If you find our model or code useful, please cite our paper:
   booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
   year={2026}
 }
-```
-
+```