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---
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}
} |