Update README.md

README.md

@@ -6,8 +6,133 @@ tags:
 - Agents
 - action prediction
 - multimodal
+- computer-use
+- video-demonstrations
+- desktop-automation
 language:
 - en
 size_categories:
 - 10K<n<100K
----
+---
+
+<p align="center">
+  <img src="assets/cua-suite-logo.png" alt="CUA-Suite Logo" width="200"/>
+</p>
+
+<h1 align="center">VideoCUA</h1>
+
+<p align="center">
+  <strong>The largest open, human annotated video corpus for desktop computer use</strong><br>
+  Part of <a href="https://cua-suite.github.io/">CUA-Suite</a>: Massive Human-annotated Video Demonstrations for Computer-Use Agents
+</p>
+
+<p align="center">
+  <a href="https://openreview.net/forum?id=IgTUGrZfMr">Paper</a> •
+  <a href="https://cua-suite.github.io/">Project Page</a> •
+  <a href="https://uivision.github.io/">UI-Vision</a> •
+  <a href="https://groundcua.github.io/">GroundCUA</a>
+</p>
+
+<p align="center">
+  <img src="assets/cua-suite-teaser.png" alt="CUA-Suite Teaser" width="100%"/>
+</p>
+
+## Overview
+
+**VideoCUA** is the largest open expert video corpus for desktop computer use, comprising **~10K tasks**, **55 hours** of continuous 30 fps screen recordings, and **6 million frames** across **87 professional desktop applications** spanning 12 categories.
+
+Unlike sparse screenshot datasets, VideoCUA preserves the full temporal dynamics of human interaction — every mouse movement, click, drag, scroll, and keystroke is logged with millisecond precision alongside continuous video. This enables research in action prediction, imitation learning, visual world models, and video-based reward modeling.
+
+VideoCUA is part of [CUA-Suite](https://cua-suite.github.io/), a unified ecosystem that also includes:
+
+- [**UI-Vision**](https://uivision.github.io/) — A rigorous desktop-centric benchmark evaluating element grounding, layout understanding, and action prediction.
+- [**GroundCUA**](https://groundcua.github.io/) — A large-scale pixel-precise UI grounding dataset with 5M+ human-verified element annotations.
+
+## Repository Structure
+
+```
+.
+├── assets/
+│   ├── cua-suite-logo.png
+│   └── cua-suite-teaser.png
+├── raw_data/                         # One zip per application (87 total)
+│   ├── 7-Zip.zip
+│   ├── Affine.zip
+│   ├── Anki.zip
+│   ├── ...
+│   └── draw.io.zip
+└── README.md
+```
+
+## Data Format
+
+Each application zip in `raw_data/` contains multiple task folders identified by numeric task IDs. Each task folder has the following structure:
+
+```
+<task_id>/
+├── action_log.json          # Task metadata and timestamped actions
+└── video/
+    ├── video.mp4            # Continuous 30 fps screen recording (1920×1080)
+    └── video_metadata.json  # Video properties (fps, duration, resolution, etc.)
+```
+
+### `action_log.json`
+
+```json
+{
+  "task_id": 45525,
+  "task_instruction": "Open test.7z present in archive it and see the contents",
+  "platform": "7-Zip",
+  "action_log": [
+    {
+      "action_type": "CLICK",
+      "timestamp": 2.581,
+      "action_params": {
+        "x": 47,
+        "y": 242,
+        "numClicks": 2
+      },
+      "groundcua_id": "9a7daeed..."
+    }
+  ]
+}
+```
+
+Each action entry includes a `groundcua_id` field — this is the unique identifier for the corresponding screenshot in the [GroundCUA](https://huggingface.co/datasets/ServiceNow/GroundCUA) repository. Using this ID, you can look up the fully annotated screenshot (with pixel-precise bounding boxes, textual labels, and semantic categories for every visible UI element) in GroundCUA, linking the video action trajectory to dense UI grounding annotations.
+
+## Citation
+
+If you find VideoCUA or any of the other works in CUA-Suite useful for your research, please cite our works:
+
+```bibtex
+@inproceedings{
+    jian2026cuasuite,
+    title={{CUA}-Suite: Massive Human-annotated Video Demonstrations for Computer-Use Agents},
+    author={Xiangru Jian and Shravan Nayak and Kevin Qinghong Lin and Aarash Feizi and Kaixin Li and Patrice Bechard and Spandana Gella and Sai Rajeswar},
+    booktitle={ICLR 2026 Workshop on Lifelong Agents: Learning, Aligning, Evolving},
+    year={2026},
+    url={https://openreview.net/forum?id=IgTUGrZfMr}
+}
+
+@inproceedings{
+    feizi2026grounding,
+    title={Grounding Computer Use Agents on Human Demonstrations},
+    author={Aarash Feizi and Shravan Nayak and Xiangru Jian and Kevin Qinghong Lin and Kaixin Li and Rabiul Awal and Xing Han L{\`u} and Johan Obando-Ceron and Juan A. Rodriguez and Nicolas Chapados and David Vazquez and Adriana Romero-Soriano and Reihaneh Rabbany and Perouz Taslakian and Christopher Pal and Spandana Gella and Sai Rajeswar},
+    booktitle={The Fourteenth International Conference on Learning Representations},
+    year={2026},
+    url={https://openreview.net/forum?id=9WiPZy3Kro}
+}
+
+@inproceedings{
+    nayak2025uivision,
+    title={{UI}-Vision: A Desktop-centric {GUI} Benchmark for Visual Perception and Interaction},
+    author={Shravan Nayak and Xiangru Jian and Kevin Qinghong Lin and Juan A. Rodriguez and Montek Kalsi and Nicolas Chapados and M. Tamer {\"O}zsu and Aishwarya Agrawal and David Vazquez and Christopher Pal and Perouz Taslakian and Spandana Gella and Sai Rajeswar},
+    booktitle={Forty-second International Conference on Machine Learning},
+    year={2025},
+    url={https://openreview.net/forum?id=5Rtj4mYH1C}
+}
+```
+
+## License
+
+This dataset is released under the [MIT License](LICENSE).