| --- |
| license: cc0-1.0 |
| task_categories: |
| - reinforcement-learning |
| - robotics |
| - image-to-video |
| - image-text-to-video |
| - image-to-3d |
| language: |
| - en |
| tags: |
| - world-model |
| - reinforcement-learning |
| - human-in-the-loop |
| - agent |
| pretty_name: No Man's Sky High-Fidelity Human-in-the-loop World Model Dataset |
| size_categories: |
| - 100K<n<1M |
| --- |
| |
| # No Man's Sky High-Fidelity Human-in-the-loop World Model Dataset |
|
|
| ## Overview |
| This dataset is designed for **world model training** using real human gameplay data from *No Man’s Sky*. |
| It captures **high-fidelity human–computer interaction** by recording both the game video and time-aligned input actions, preserving the realistic latency characteristics of a human-in-the-loop system. |
|
|
| Compared with “internal game state” datasets, this dataset retains the **physical interaction chain** (input → game/render → screen → capture), making it well-suited for training models that need to operate under real-world latency and sensory constraints. |
|
|
| ## Dataset Structure |
| Each recording session is stored in a UUID directory. |
| A typical session contains: |
| <UUID>/ |
| recording.mp4 |
| actions.jsonl |
| events.jsonl |
| metadata.json |
| actions_resampled.jsonl |
| |
| ### 1) `recording.mp4` |
| The recorded gameplay video. |
| |
| ### 2) `actions.jsonl` (per-frame input state) |
| One JSON object per video frame. Each entry contains the input state sampled at frame time. |
| |
| **Schema:** |
| - `frame` (int): frame index |
| - `timestamp_ms` (int): wall-clock timestamp in milliseconds |
| - `frame_pts_ms` (float): frame time in milliseconds (PTS-based) |
| - `capture_ns` (int): OBS compositor timestamp in nanoseconds |
| - `key` (string[]): list of pressed keys at this frame |
| - `mouse` (object): |
| - `dx` (int): accumulated mouse delta X during the frame |
| - `dy` (int): accumulated mouse delta Y during the frame |
| - `x` (int): absolute mouse X position |
| - `y` (int): absolute mouse Y position |
| - `scroll_dy` (int): scroll delta during the frame |
| - `button` (string[]): pressed mouse buttons (e.g., `LeftButton`, `Button4`) |
|
|
| ### 3) `events.jsonl` (raw sub-frame input events) |
| Raw input events with microsecond timing, captured from the OS event stream. |
|
|
| **Schema:** |
| - `type` (string): event type |
| - `key_down`, `key_up`, `flags_changed` |
| - `mouse_move`, `mouse_button_down`, `mouse_button_up` |
| - `scroll` |
| - `timestamp_ms` (int): wall-clock timestamp |
| - `session_offset_us` (int): microsecond offset from session start |
| - `key` (string): key name for key events |
| - `button` (string): mouse button name |
| - `dx`, `dy`, `x`, `y` (int): mouse movement |
| - `scroll_dy` (int): scroll delta |
|
|
| ### 4) `metadata.json` |
| Session-level metadata and video info. |
|
|
| **Schema:** |
| - `stream_name` (string): session UUID |
| - `game_name` (string): game name |
| - `platform` (string): `mac` / `windows` / `linux` |
| - `video_meta` (object): |
| - `width` (int) |
| - `height` (int) |
| - `fps` (float) |
| - `total_frames` (int) |
| - `duration_ms` (int) |
| - `input_latency_bias_ms` (number): recommended latency bias for alignment |
|
|
| ### 5) `actions_resampled.jsonl` |
| High-precision resampled per-frame actions reconstructed from `events.jsonl` using latency compensation. |
| This is the recommended aligned input stream for training. |
| |
| --- |
| |
| ## Suggested Usage |
| - For **world model training**, use `recording.<ext>` + `actions_resampled.jsonl`. |
| - For **analysis or recalibration**, use `events.jsonl` and `metadata.json`. |
|
|
| --- |
|
|
| ## Notes |
| - The dataset captures realistic system latency; alignment is provided but does **not** remove physical pipeline delay. |
| - This design targets **high-fidelity human-in-the-loop interaction** for robust world-model learning. |
|
|
