High-fidelity, tick-aligned gameplay data matters because it bridges simulator/demo ticks and frame-level media needed for both perception models and control/RL agents. OpenCS2 supplies synchronized POV videos plus per-tick inputs and world-state, enabling supervised and self-supervised tasks that require exact temporal alignment between actions, observations, and events.
What Sets It Apart
- Tick-to-frame synchronization and per-tick sidecars: every POV round includes a ticks.parquet with inputs (keys, mouse deltas, view angles), world state (position, velocity, health, weapon) and a timestamp t aligned to the rendered MP4. So what: you can precisely join events (kills/duels) to video frames for supervised labeling or imitation learning.
- Media + compact parquet indexes: media is stored as MP4s (1280×720 @ 32fps, near-lossless) and the dataset exposes multiple parquet indexes (pov_rounds, rounds, matches, kills, duels, clip_events) for fast DuckDB-style filtering. So what: filter on events or metadata before streaming media, dramatically reducing I/O for large-scale extraction.
- Multi-view per-round (10 POVs) and per-player audio: each round contains ten synchronized player POVs with per-player stereo audio. So what: supports multi-agent and multi-view research (e.g., cross-view correspondence, team-level behaviors) and audio-visual tasks.
- Production-scale size and recipes: hundreds of thousands of POV rounds (~165k POV rounds / thousands of video hours) plus verified extraction recipes (AWP 1v1, through-smoke, long-distance kills, frame-pair sampling). So what: ready for large-scale training pipelines and reproducible clip-mining.
Who It's For
Great fit if you develop video-based RL agents, imitation learning pipelines, event-driven clip extraction, or video understanding models that need precise action–frame alignment. Researchers needing per-tick controls, frame-pair supervision, or multi-view synchronization will find the parquet indices and WDS packaging especially useful. Look elsewhere if you require anonymized real-world human video (this dataset is game renders and sourced from HLTV demos subject to tournament terms), or if you need tiny datasets for quick prototyping — OpenCS2 is built for medium-to-large scale experiments.
Where It Fits
OpenCS2 is a specialized dataset in the game-simulation / esports domain: it sits between raw demo logs (high-frequency but not media) and general-purpose video corpora (media without per-tick inputs). Its primary value is precise temporal coupling of control inputs, world state, and rendered frames — a niche that speeds up imitation learning, clip mining, and audiovisual research on competitive FPS gameplay.
