| --- |
| license: other |
| license_name: gg800-subset |
| license_link: LICENSE |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: |
| - train/metadata.csv |
| - train/**/capture.mkv |
| size_categories: |
| - 1K<n<10K |
| task_categories: |
| - video-classification |
| tags: |
| - gaming |
| - gta5 |
| --- |
| |
| # GG800-Subset 🎮✨ |
|
|
| Welcome to **GG800-Subset** - a clean, action-conditioned GTA5 trajectory subset for world-model research. |
| Built with love at LucidML 💙 |
|
|
| --- |
|
|
| ## What is this? |
|
|
| GG800-Subset is a curated public slice of the larger GG800 pipeline. |
| Each sample is a synchronized triplet: |
|
|
| - `capture.mkv` -> the video clip |
| - `events.srt` -> human-readable control timeline overlay |
| - `events.jsonl` -> frame-level machine-readable control states |
|
|
| The key contract: **frame and controls are aligned by frame index** (not loose timestamp matching), so training pipelines can reliably map visuals <-> actions. |
|
|
| ### What the footage actually is 🎥 |
|
|
| GG800-Subset contains action-agent gameplay footage from GTA V collected with a bias-reduction collection policy: |
|
|
| - capture environment uses **GTA5 Enhanced** with the **NaturalVision Enhanced (NVE)** visual mod for higher-fidelity graphics; |
| - each clip starts from a randomized world spawn location; |
| - the agent is reset into either a random pedestrian state or a random vehicle context (for example car/bike/other road vehicle); |
| - actions are programmatically generated to avoid repetitive route-locked behavior and improve behavioral coverage. |
|
|
| Each clip follows a fixed format: |
|
|
| - resolution: **720p** |
| - frame rate: **12 FPS** |
| - clip length: **384 frames** (32 seconds) |
| - `events.jsonl`: **exactly 384 lines**, one per frame |
|
|
| So the alignment invariant is strict: |
| **frame `i` in video <-> line `i` in `events.jsonl`** for `i = 0..383`. |
|
|
| Practical note: the first ~2 seconds (~24 frames) may include scene/asset loading artifacts in some clips. |
| For model training/inference pipelines, a common practice is to skip these initial frames and use the final **360** frames. |
| Even when you apply this trim, action logs remain perfectly synchronized with frames by index. |
|
|
| --- |
|
|
| ## Dataset Format (Simple + Practical) 📦 |
|
|
| Each row corresponds to one clip directory with three assets: |
|
|
| - `video` (`.mkv`) |
| - `srt_file_name` (`.srt`) |
| - `action_file_name` (`.jsonl`) |
|
|
| In `events.jsonl`, each line includes: |
|
|
| - `frame` (frame index) |
| - `t` (seconds) |
| - analog controls (`lx`, `ly`, `rx`, `ry`, `lt`, `rt`) |
| - button state list (`buttons`) |
| - optional context fields like reset/mode metadata |
|
|
| This is designed so you can stream rows directly into action-conditioned training code with minimal preprocessing. |
|
|
| --- |
|
|
| ## Why this is useful for world models 🧠 |
|
|
| - Action-conditioned trajectories (not just passive video) |
| - Strong temporal alignment between controls and frames |
| - Mixed contexts (on-foot + vehicle segments) |
| - Collected for long-horizon, interactive modeling workflows |
|
|
| --- |
|
|
| ## Quick Start (Python) 🚀 |
|
|
| ```python |
| from datasets import load_dataset |
| import json |
| from huggingface_hub import hf_hub_download |
| |
| ds = load_dataset("lucidml/GG800-Subset", split="train") |
| sample = ds[0] |
| |
| # `video` is a Video feature object/handle. |
| video_obj = sample["video"] |
| |
| # Sidecar files are stored as relative paths in the dataset repo. |
| srt_path = hf_hub_download( |
| repo_id="lucidml/GG800-Subset", |
| repo_type="dataset", |
| filename=f"train/{sample['srt_file_name']}", |
| ) |
| jsonl_path = hf_hub_download( |
| repo_id="lucidml/GG800-Subset", |
| repo_type="dataset", |
| filename=f"train/{sample['action_file_name']}", |
| ) |
| |
| with open(jsonl_path, "r", encoding="utf-8") as f: |
| first_event = json.loads(f.readline()) |
| |
| print("first frame event:", first_event) |
| ``` |
|
|
| --- |
|
|
| ## License and Access 🔐 |
|
|
| - GG800-Subset is released under the repository `LICENSE` file (research-focused terms). |
| - For larger subsets, enterprise access, or full-dataset discussions, contact: |
| - `abhishek@lucidml.ai` |
| - Subject: `GG800 Access Inquiry` |
|
|
| --- |
|
|
| ## Citation 📝 |
|
|
| If you use GG800-Subset in research, please cite the associated paper/technical report and mention: |
|
|
| > "This work uses GG800-Subset, licensed under the GG800-Subset Research License v1.0." |
|
|
| --- |
|
|
| Thanks for building with GG800-Subset. |
| If you create something cool, we would love to see it! 🌍⚡ |
