---
license: mit
language:
- en
tags:
- video
- video understanding
- game
- gameplay understanding
- multi-agent
- esport
- counter-strike
- opponent-modeling
- ego-centric
- cross ego-centric
task_categories:
- video-classification
- video-text-to-text
- visual-question-answering
- text-to-video
pretty_name: X-EGO
---
# Dataset Card for X-Ego-CS

Links:
- [Paper](https://arxiv.org/abs/2510.19150)
- [Github Codebase](https://github.com/HATS-ICT/x-ego)
- Homepage (comming soon)

## Cross-Ego Demo (Pistol Round)
<video controls>
  <source src="https://huggingface.co/datasets/wangyz1999/X-EGO-CS/resolve/main/multi-ego-sync-demo-pistol.mp4" type="video/mp4">
</video>

**Note:** This demo concats videos in a grid. The original datasets videos are from individual player POV recording.

## Dataset Summary
**X-Ego-CS** is a multi-agent gameplay video dataset for **cross-egocentric multi-agent video understanding** in Counter-Strike:2. It contains **124 hours** of synchronized first-person gameplay footage captured from **45 professional-level Counter-Strike 2 matches**. Each match includes **multi-player egocentric video streams** (POVs from all players) and corresponding **state-action trajectories**, enabling the study of **team-level tactical reasoning** and **situational awareness** from individual perspectives.

The dataset was introduced in the paper:

> **X-Ego: Acquiring Team-Level Tactical Situational Awareness via Cross-Egocentric Contrastive Video Representation Learning**  
> *Yunzhe Wang, Soham Hans, Volkan Ustun*  
> University of Southern California, Institute for Creative Technologies (2025)  
> [arXiv:2510.19150](https://arxiv.org/abs/2510.19150)

X-Ego-CS supports research on **multi-agent representation learning**, **egocentric video modeling**, **team tactic analysis**, and **AI-human collaboration** in complex 3D environments.
---

## How to Download 

To download the full dataset using the Hugging Face CLI:

```bash
# Install the Hugging Face Hub client
pip install --upgrade huggingface_hub

# (Optional) Log in if the dataset is private
huggingface-cli login

# Download the dataset repository
huggingface-cli download wangyz1999/X-EGO-CS \
  --repo-type dataset \
  --local-dir ./X-EGO-CS \
  --resume-download \
  --max-workers 8
```

## Dataset Structure

### Data Fields

**Segment Info**
- `idx` — Row index (unique for each segment)  
- `partition` — Dataset split label (e.g., train/test/val)  
- `seg_duration_sec` — Duration of the segment in seconds  
- `start_tick`, `end_tick`, `prediction_tick` — Game tick indices for start, end, and prediction points  
- `start_seconds`, `end_seconds`, `prediction_seconds` — Corresponding timestamps in seconds  
- `normalized_start_seconds`, `normalized_end_seconds`, `normalized_prediction_seconds` — Time values normalized to a [0–1] scale for model input

**Match Metadata**
- `match_id` — Unique identifier of the match  
- `round_num` — Match round number  
- `map_name` — Name of the game map (e.g., *de_mirage*)

**Player States** (for `player_0` → `player_9`)
- `player_{i}_id` — Unique identifier (e.g., Steam ID)  
- `player_{i}_name` — In-game player name  
- `player_{i}_side` — Team side (`t` for Terrorist, `ct` for Counter-Terrorist)  
- `player_{i}_X`, `player_{i}_Y`, `player_{i}_Z` — Player’s position coordinates (normalized or map-based)  
- `player_{i}_place` — Named location or area on the map (e.g., *CTSpawn*, *SideAlley*)


## File Structure
```
data/
├── demos/                       # Raw .dem files (by match)
│   └── <match_id>.dem
├── labels/                      # Global label datasets
│   ├── enemy_location_nowcast_s1s_l5s.csv
│   └── teammate_location_nowcast_s1s_l5s.csv
├── metadata/                    # Match / round metadata
│   ├── matches/
│   │   └── <match_id>.json
│   └── rounds/
│       └── <match_id>/
│           └── round_<nn>.json
├── trajectories/                # Player movement trajectories
│   └── <match_id>/
│       └── <player_id>/
│           ├── round_<nn>.csv
│           └── ...
└── videos/                      # Player POV recordings
    └── <match_id>/
        └── <player_id>/
            ├── round_<nn>.mp4
            └── ...
```
---

## Dataset Creation

### Curation Rationale
The dataset was designed to study **cross-perspective alignment** in team-based tactical games where each player’s view provides only a partial observation of the environment. 
Synchronizing multiple first-person streams allows for modeling **shared situational awareness** and **implicit coordination**—key ingredients in human team intelligence.

### Source Data
- **Game:** Counter-Strike 2 (Valve Corporation) in-game demo replay recording. Downloaded from top elo-leaderboard from [Faceit.com](https://www.faceit.com/)
- **Recording setup:** Screen capture of first-person gameplay, synchronized across all agents using timestamp alignment  
- **Annotations:** Automatically generated state-action trajectories derived from server replay data  
---

## Dataset Statistics
- **Total hours:** 124  
- **Total matches:** 45  
- **Agents per match:** 10 (5 per team)  
- **Frame rate:** 30 fps  
- **Video resolution:** 1080x720  

---

## Citation

If you use this dataset, please cite the following paper:

```bibtex
@article{wang2025x,
  title={X-Ego: Acquiring Team-Level Tactical Situational Awareness via Cross-Egocentric Contrastive Video Representation Learning},
  author={Wang, Yunzhe and Hans, Soham and Ustun, Volkan},
  journal={arXiv preprint arXiv:2510.19150},
  year={2025}
}