README.md

metadata

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: / 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.