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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! 🌍⚡ |