X-Ego-CS: 124 Hours of Synchronized First-Person Counter-Strike Gameplay

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 players. The dataset was authored by wangyz1999 and last updated on 2025-11-12.

Use Cases

• Train multi-agent action recognition models based on synchronized first-person video streams.
• Develop cross-egocentric view synthesis algorithms based on multiple concurrent player perspectives.
• Analyze team coordination and player behavior based on synchronized gameplay footage.
• Benchmark video understanding tasks for multi-agent environments based on the Counter-Strike gameplay context.

Strengths

• Contains 124 hours of video footage.
• Includes synchronized first-person recordings from 45 individual players.
• Specifically designed for cross-egocentric multi-agent understanding tasks.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Data may reflect bias inherent to the specific game and player sample.

Provenance

Source

huggingface

Collection Method

First-person gameplay footage captured from players in Counter-Strike:2.

Freshness

Last updated 2025-11-12 12:26:46; freshness should be verified.