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---
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.
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