Update README.md

README.md

@@ -8,12 +8,51 @@ viewer: false
 
 # MASIV Multi-Sequence Dataset
 
+> **Toward Material-Agnostic System Identification from Videos**
+> 
+> [Yizhou Zhao](https://scholar.google.com/citations?user=nVKRaf4AAAAJ&hl=en)<sup>1</sup>, [Haoyu Chen](https://tonychen050400.github.io/)<sup>1</sup>, [Chunjiang Liu](https://chunjiangliu.com/)<sup>1</sup>, [Zhenyang Li](https://scholar.google.com/citations?hl=en&user=r9f4mLMAAAAJ)<sup>2</sup>, [Charles Herrmann](https://scholar.google.com/citations?user=LQvi5XAAAAAJ&hl=en)<sup>3</sup>, [Junhwa Hur](https://hurjunhwa.github.io/)<sup>3</sup>, [Yinxiao Li](https://scholar.google.com/citations?user=kZsIU74AAAAJ&hl=en)<sup>3</sup>, [Ming‑Hsuan Yang](https://scholar.google.com/citations?user=p9-ohHsAAAAJ&hl=en)<sup>4</sup>, [Bhiksha Raj](https://scholar.google.com/citations?user=IWcGY98AAAAJ&hl=en)<sup>1</sup>, [Min Xu](https://scholar.google.com/citations?user=Y3Cqt0cAAAAJ&hl=en)<sup>1*</sup>
+> 
+> <sup>1</sup>Carnegie Mellon University  <sup>2</sup>University of Alabama at Birmingham  <sup>3</sup>Google  <sup>4</sup>UC Merced
+
 [![arXiv](https://img.shields.io/badge/-arXiv-b31b1b?logo=arxiv&logoColor=white&style=flat-square)](https://arxiv.org/abs/2508.01112)
 [![GitHub](https://img.shields.io/badge/-GitHub-black?logo=git&logoColor=white&style=flat-square)](https://github.com/Skaldak/MASIV)
 
-The dataset was generated with [Genesis](https://genesis-embodied-ai.github.io/), incorporating
-  - 10 objects of 5 materials
-  - 10 sequences per object
-  - 11 views per sequence
+## Introduction
+
+The MASIV Multi-Sequence Dataset is a synthetic dataset generated by [Genesis](https://genesis-embodied-ai.github.io/) to evaluate the generalization of data-driven 
+constitutive models. The dataset contains 10 objects of 5 distinct materials (Elastic, Elastoplastic, Liquid, Sand, and Snow), with each object including 10
+different multi-view sequences. These sequences have randomized initial conditions for location, pose, and velocity. Each sequence includes 11
+views, with each view consisting of 30 frames.
+
+![](https://github.com/Skaldak/MASIV/blob/main/assets/dataset.png?raw=true)
+
+## Dataset Structure
+
+```
+MASIV/
+├── 0_0/                                            # <ObjectID>_<SequenceID>
+  ├── data/                                         # Contains per-frame images
+  ├── point_clouds/                                 # Stores point cloud data for 30 frames
+  ├── videos/                                       # Stores 11 videos, one for each view
+  ├── all_data.json                                 # A JSON file containing camera information
+  ├── metadata.json                                 # A JSON file containing global simulation and object-specific parameters
+  ├── transforms_test.json                          # Contains the camera transformation matrices and file paths for the images in the test set
+  ├── transforms_train.json                         # Contains the camera transformation matrices and file paths for the images in the training set
+  ├── transforms_val.json                           # Contains the camera transformation matrices and file paths for the images in the validation set
+├── 0_1/
+...
+├── 9_9/ 
+```
+
+## Citing MASIV
+
+If you find this dataset useful in your work, please consider citing our paper:
 
-![](https://github.com/Skaldak/MASIV/blob/main/assets/dataset.png?raw=true)
+```
+@article{zhao2025masiv,
+  title={MASIV: Toward Material-Agnostic System Identification from Videos},
+  author={Zhao, Yizhou and Chen, Haoyu and Liu, Chunjiang and Li, Zhenyang and Herrmann, Charles and Hur, Junhwa and Li, Yinxiao and Yang, Ming-Hsuan and Raj, Bhiksha and Xu, Min},
+  journal={arXiv preprint arXiv:2508.01112},
+  year={2025}
+}
+```