Update README.md

README.md

@@ -10,9 +10,9 @@ tags:
 
 # D2E-480p
 
-This is the dataset for [**D2E: Scaling Vision-Action Pretraining on Desktop Data for Transfer to Embodied AI**](https://worv-ai.github.io/d2e/).
+[Project Page](https://worv-ai.github.io/d2e/) · [Paper (arXiv)](https://arxiv.org/abs/2510.05684) · [GitHub](https://github.com/worv-ai/D2E) · [OWA Toolkit Documentation](https://open-world-agents.github.io/open-world-agents/)
 
-267 hours of synchronized video, audio, and input events from 29 PC games, for training vision-action models and game agents.
+This is the dataset for [**D2E: Scaling Vision-Action Pretraining on Desktop Data for Transfer to Embodied AI**](https://worv-ai.github.io/d2e/). **267 hours** of synchronized video, audio, and input events from **29 PC games** across diverse genres (FPS, open-world, sandbox, and more), for training vision-action models and game agents.
 
 **What's included:**
 
@@ -20,29 +20,19 @@ This is the dataset for [**D2E: Scaling Vision-Action Pretraining on Desktop Dat
 - **Input events**: Keyboard press/release, raw mouse deltas (bypasses pointer acceleration), mouse clicks, and active window info—all with nanosecond timestamps synchronized to video frames.
 - **[OWAMcap](https://open-world-agents.github.io/open-world-agents/data/getting-started/why-owamcap/) format**: Built on [MCAP](https://mcap.dev/) (widely adopted in robotics). Indexed for fast random access, crash-safe writes, and standardized message schemas that work across different datasets without custom parsing.
 
-**Recommended for:**
-
-- Training game agents with vision-action trajectories
-- Pretraining vision-action models for transfer to embodied AI (robotic manipulation, navigation)
-- World model / video generation training (use [D2E-Original](https://huggingface.co/datasets/open-world-agents/D2E-Original) for HD/QHD)
+**Recommended for:** Training game agents with vision-action trajectories, pretraining vision-action models for transfer to embodied AI (robotic manipulation, navigation), or world model / video generation training (use [D2E-Original](https://huggingface.co/datasets/open-world-agents/D2E-Original) for HD/QHD).
 
 > ⚠️ **December 1, 2025**: Dataset revised due to sync issues. Re-download if you obtained data before this date.
 
 ## Visualize
 
-Explore recordings directly in your browser with synchronized keyboard/mouse overlay:
+Explore recordings directly in your browser with synchronized keyboard/mouse overlay: **👉 [Open in Dataset Visualizer](https://huggingface.co/spaces/open-world-agents/visualize_dataset?repo_id=open-world-agents/D2E-480p)**
 
-**👉 [Open in Dataset Visualizer](https://huggingface.co/spaces/open-world-agents/visualize_dataset?repo_id=open-world-agents/D2E-480p)**
+<img src="https://open-world-agents.github.io/open-world-agents/data/examples/viewer.png" alt="Dataset Visualizer Preview" width="600">
 
 ## Load the data
 
-Install dependencies:
-
-| Package            | Description                                                    |
-| ------------------ | -------------------------------------------------------------- |
-| `mcap-owa-support` | Reader for OWAMcap files                                       |
-| `owa-msgs`         | Message type definitions (`KeyboardEvent`, `MouseEvent`, etc.) |
-| `huggingface_hub`  | Download files from this dataset                               |
+Install `mcap-owa-support` (OWAMcap reader), `owa-msgs` (message type definitions), and `huggingface_hub`:
 
 ```bash
 pip install mcap-owa-support owa-msgs huggingface_hub
@@ -76,13 +66,9 @@ with OWAMcapReader(mcap_file) as reader:
         break
 ```
 
-**Ready to train?**
-
-We provide [owa-data](https://github.com/open-world-agents/open-world-agents/tree/main/projects/owa-data), a data pipeline that converts this dataset into HuggingFace Datasets ready for PyTorch DataLoader. It handles tokenization and sequence packing out of the box—so you can start training immediately without writing custom data loading code.
-
-**Learn more:**
+**Learn more:** [OWAMcap format guide](https://open-world-agents.github.io/open-world-agents/data/technical-reference/format-guide/)
 
-- [OWAMcap format guide](https://open-world-agents.github.io/open-world-agents/data/technical-reference/format-guide/)
+**For training:** We provide [owa-data](https://github.com/open-world-agents/open-world-agents/tree/main/projects/owa-data), a data pipeline that converts this dataset into HuggingFace Datasets ready for **PyTorch DataLoader**. It handles tokenization and sequence packing out of the box—so you can start training immediately without writing custom data loading code.
 
 ## Structure
 
@@ -97,9 +83,7 @@ Apex_Legends/
 └── ...
 ```
 
-The `.mcap` file stores lightweight [MediaRef](https://github.com/open-world-agents/MediaRef) pointers to video frames instead of raw pixels—frames are decoded on-demand from the `.mkv` when you call `load_frame_array()`.
-
-MCAP files contain timestamped messages on these topics:
+The `.mcap` file stores lightweight [MediaRef](https://github.com/open-world-agents/MediaRef) pointers to video frames instead of raw pixels—frames are decoded on-demand from the `.mkv` when you call `load_frame_array()`. MCAP files contain timestamped messages on these topics:
 
 | Topic            | Message Type             | Description                                                                                                                 |
 | ---------------- | ------------------------ | --------------------------------------------------------------------------------------------------------------------------- |
@@ -113,9 +97,7 @@ MCAP files contain timestamped messages on these topics:
 
 ## Games
 
-Genres: FPS (Apex Legends, PUBG), open-world (Cyberpunk 2077, GTA V), simulation (Euro Truck Simulator 2), sandbox (Minecraft), roguelike (Brotato, Vampire Survivors), and more.
-
-29 games released (~267h) from 31 games collected (~335h) after privacy filtering.
+Genres: FPS (Apex Legends, PUBG), open-world (Cyberpunk 2077, GTA V), simulation (Euro Truck Simulator 2), sandbox (Minecraft), roguelike (Brotato, Vampire Survivors), and more. 29 games released (267h) from 31 games collected (335h) after privacy filtering.
 
 | Game                   | Hours | Sessions |
 | ---------------------- | ----: | -------: |
@@ -151,12 +133,6 @@ Genres: FPS (Apex Legends, PUBG), open-world (Cyberpunk 2077, GTA V), simulation
 
 For HD/QHD resolution, see [D2E-Original](https://huggingface.co/datasets/open-world-agents/D2E-Original).
 
-## Links
-
-- [Project Page](https://worv-ai.github.io/d2e/)
-- [Paper (arXiv)](https://arxiv.org/abs/2510.05684)
-- [GitHub](https://github.com/worv-ai/D2E)
-- [OWA Toolkit Documentation](https://open-world-agents.github.io/open-world-agents/)
 
 ## Citation