Understanding Emergent Non-Verbal Communication in the Delta Force Competitive Video Game through Multimodal AI Analysis

Researchers at an unnamed institution have developed a multimodal AI pipeline to analyze emergent non-verbal communication in the competitive video game Delta Force, marking a shift from prior studies focused on MOBA games like League of Legends and Dota 2. The work extracts gestures, movement patterns, and item interactions from gameplay videos to study how players coordinate without verbal cues in extraction shooter and battle royale genres.

Non-verbal communication plays a critical role in multiplayer games, players often rely on gestures, movement patterns, item interactions, and UI signals to communicate intent, negotiate cooperation willingness, and avoid conflict. May et al. May et al. 2013 https://dl.acm.org/doi/10.1145/3799828.3816006 core-collateral-Bib0007 demonstrated how non-verbal game behaviors are effective at changing player behavior and task success. Many studies have considered non-verbal communication in competitive esports, though the bulk of them have focused on MOBA games like League of Legends Leavitt et al. 2016 https://dl.acm.org/doi/10.1145/3799828.3816006 core-collateral-Bib0005 , Dota 2 Wuertz et al. 2017 https://dl.acm.org/doi/10.1145/3799828.3816006 core-collateral-Bib0014 , and Heroes of the Storm Zheng and Farzan 2023 https://dl.acm.org/doi/10.1145/3799828.3816006 core-collateral-Bib0015 ; Zheng et al. 2023 https://dl.acm.org/doi/10.1145/3799828.3816006 core-collateral-Bib0016 . MOBA games include a ping feature, that places a mark on the map with intention to communicate warning or helping concepts. While players develop their own intent with these tools, ping largely sticks to the game designer’s intended meaning, and are somewhat more limited in scope than more expressive non-verbal communication. Another line of research has also leveraged MOBAs and online matchmaking that matches team mates with strangers to evaluate how ad hoc teams work Kou and Gui 2014 https://dl.acm.org/doi/10.1145/3799828.3816006 core-collateral-Bib0004 ; Lee et al. 2025 https://dl.acm.org/doi/10.1145/3799828.3816006 core-collateral-Bib0006 ; Tan et al. 2022 https://dl.acm.org/doi/10.1145/3799828.3816006 core-collateral-Bib0012 . However, other genres, like extraction shooter and free-for-all battle royale games, have enabled even more ad hoc teaming, betrayals, and rescue behavior. In most shooter games, teams generally coordinate through verbal communication such as map callouts in Counter-Strike Rusk et al. 2024 https://dl.acm.org/doi/10.1145/3799828.3816006 core-collateral-Bib0010 ; Tang et al. 2012 https://dl.acm.org/doi/10.1145/3799828.3816006 core-collateral-Bib0013 . In contrast to these prior studies, we endeavor to study non-verbal communication in the shooter game genre. In our work, we analyze gameplay videos to extract non-verbal communication. Some past work has shown that training a model with data across a range of shooter games it is capable of learning some cross-game features due to the similarities within the genre. Rašajski et al. 2024 https://dl.acm.org/doi/10.1145/3799828.3816006 core-collateral-Bib0009 . Perhaps the closest related work to our method is GELID, which performs a somewhat different set of operations, including segmenting based on captions, categorizing and grouping with image features and color analysis Guglielmi et al. 2023 https://dl.acm.org/doi/10.1145/3799828.3816006 core-collateral-Bib0003 . Others have shown more general video game understanding using VLMs Taesiri and Bezemer 2025 https://dl.acm.org/doi/10.1145/3799828.3816006 core-collateral-Bib0011 . We believe our pipeline to be novel, while being composed of relatively well-understood components.