--- license: cc-by-nc-4.0 tags: - vision-action - embodied-ai - game-dataset - imitation-learning - pretraining - OWA task_categories: - robotics --- # D2E-Original [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/) This is the dataset for [**D2E: Scaling Vision-Action Pretraining on Desktop Data for Transfer to Embodied AI**](https://worv-ai.github.io/d2e/). **273.4 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:** - **Video + Audio**: H.264 encoded at FHD/QHD 60fps with game audio. - **Input events**: Keyboard (press/release + key state), mouse (clicks, screen coordinates, raw HID deltas, button state), 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 world model/video generation model. For training game agents with vision–action trajectories or pretraining vision–action models for transfer to embodied AI (e.g., robotic manipulation and navigation), we recommend using [D2E-480p](https://huggingface.co/datasets/open-world-agents/D2E-480p) for efficient training at 480p resolution. > ⚠️ **2026/01/14**: We’ve completed fixes for issues introduced during reprocessing/re-filtering after applying privacy filtering (e.g., timestamps, MKV file paths). If you downloaded the data before this date, please re-download the updated version. ## Visualize 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-Original)** Dataset Visualizer Preview ## Load the data Install `mcap-owa-support` (OWAMcap reader), `owa-msgs` (message type definitions), and `huggingface_hub`: ```bash pip install mcap-owa-support owa-msgs huggingface_hub ``` Then load and iterate through the data: ```python from huggingface_hub import hf_hub_download from mcap_owa.highlevel import OWAMcapReader mcap_file = hf_hub_download( repo_id="open-world-agents/D2E-Original", filename="Apex_Legends/0805_01.mcap", repo_type="dataset" ) with OWAMcapReader(mcap_file) as reader: for msg in reader.iter_messages(topics=["screen"]): screen = msg.decoded screen.resolve_relative_path(mcap_file) # Resolve video path relative to mcap frame = screen.load_frame_array() # numpy array (H, W, 3) break for msg in reader.iter_messages(topics=["keyboard"]): print(msg.decoded) # KeyboardEvent(event_type='press', vk=87) break for msg in reader.iter_messages(topics=["mouse/raw"]): print(msg.decoded) # RawMouseEvent(last_x=12, last_y=-3, button_flags=0) break ``` **Learn more:** [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 Each game folder contains paired `.mcap` + `.mkv` files: ``` Apex_Legends/ ├── 0805_01.mcap # Timestamped events + frame references ├── 0805_01.mkv # Video + audio (FHD/QHD 60fps, H.264) ├── 0805_02.mcap ├── 0805_02.mkv └── ... ``` 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 | | ---------------- | ------------------------ | --------------------------------------------------------------------------------------------------------------------------- | | `screen` | `desktop/ScreenCaptured` | Frame timestamp + MediaRef pointer to video | | `keyboard` | `desktop/KeyboardEvent` | Key press/release with [virtual key code](https://learn.microsoft.com/en-us/windows/win32/inputdev/virtual-key-codes) | | `keyboard/state` | `desktop/KeyboardState` | Currently pressed keys | | `mouse` | `desktop/MouseEvent` | Mouse clicks and screen coordinates | | `mouse/raw` | `desktop/RawMouseEvent` | Raw HID movement ([→ why raw?](https://open-world-agents.github.io/open-world-agents/env/plugins/desktop/#raw-mouse-input)) | | `mouse/state` | `desktop/MouseState` | Current position and button state | | `window` | `desktop/WindowInfo` | Active window title, rect, and handle | ## 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 (273h) from 31 games collected (335h) after privacy filtering. | Game | Hours | | ---------------------- | ----: | | Apex Legends | 25.6 | | Euro Truck Simulator 2 | 19.6 | | Eternal Return | 17.1 | | Stardew Valley | 14.6 | | Cyberpunk 2077 | 14.2 | | MapleStory Worlds | 14.1 | | Rainbow Six | 13.7 | | Grand Theft Auto V | 12.9 | | Slime Rancher | 10.7 | | Dinkum | 10.4 | | Medieval Dynasty | 10.9 | | Raft | 10.8 | | Counter-Strike 2 | 9.9 | | Satisfactory | 9.8 | | Grounded | 9.7 | | Ready Or Not | 9.6 | | Barony | 9.3 | | Core Keeper | 8.9 | | Minecraft | 8.6 | | Monster Hunter Wilds | 7.9 | | Brotato | 6.0 | | PUBG | 4.9 | | Vampire Survivors | 2.8 | | Battlefield 6 | 2.2 | | Skul | 2.0 | | PEAK | 1.8 | | OguForest | 0.8 | | Super Bunny Man | 0.7 | | VALORANT | 0.3 | For 480p resolution, see [D2E-480p](https://huggingface.co/datasets/open-world-agents/D2E-480p). ## Citation ```bibtex @article{choi2025d2e, title={D2E: Scaling Vision-Action Pretraining on Desktop Data for Transfer to Embodied AI}, author={Choi, Suhwan and Jung, Jaeyoon and Seong, Haebin and Kim, Minchan and Kim, Minyeong and Cho, Yongjun and Kim, Yoonshik and Park, Yubeen and Yu, Youngjae and Lee, Yunsung}, journal={arXiv preprint arXiv:2510.05684}, year={2025} } ```