Update README.md

README.md

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----
-license: cc0-1.0
----
+---
+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.