Joohwan Kim

David Durst, Feng Xie, Vishnu Sarukkai et al.

In multiplayer, first-person shooter games like Counter-Strike: Global Offensive (CS:GO), coordinated movement is a critical component of high-level strategic play. However, the complexity of team coordination and the variety of conditions present in popular game maps make it impractical to author hand-crafted movement policies for every scenario. We show that it is possible to take a data-driven approach to creating human-like movement controllers for CS:GO. We curate a team movement dataset comprising 123 hours of professional game play traces, and use this dataset to train a transformer-based movement model that generates human-like team movement for all players in a "Retakes" round of the game. Importantly, the movement prediction model is efficient. Performing inference for all players takes less than 0.5 ms per game step (amortized cost) on a single CPU core, making it plausible for use in commercial games today. Human evaluators assess that our model behaves more like humans than both commercially-available bots and procedural movement controllers scripted by experts (16% to 59% higher by TrueSkill rating of "human-like"). Using experiments involving in-game bot vs. bot self-play, we demonstrate that our model performs simple forms of teamwork, makes fewer common movement mistakes, and yields movement distributions, player lifetimes, and kill locations similar to those observed in professional CS:GO match play.

Ekta Prashnani, Orazio Gallo, Joohwan Kim et al.

We tackle the problem of predicting saliency maps for videos of dynamic scenes. We note that the accuracy of the maps reconstructed from the gaze data of a fixed number of observers varies with the frame, as it depends on the content of the scene. This issue is particularly pressing when a limited number of observers are available. In such cases, directly minimizing the discrepancy between the predicted and measured saliency maps, as traditional deep-learning methods do, results in overfitting to the noisy data. We propose a noise-aware training (NAT) paradigm that quantifies and accounts for the uncertainty arising from frame-specific gaze data inaccuracy. We show that NAT is especially advantageous when limited training data is available, with experiments across different models, loss functions, and datasets. We also introduce a video game-based saliency dataset, with rich temporal semantics, and multiple gaze attractors per frame. The dataset and source code are available at https://github.com/NVlabs/NAT-saliency.

Josef Spjut, Ben Boudaoud, Joohwan Kim

Lower computer system input-to-output latency substantially reduces many task completion times. In fact, literature shows that reduction in targeting task completion time from decreased latency often exceeds the decrease in latency alone. However, for aiming in first person shooter (FPS) games, some prior work has demonstrated diminishing returns below 40 ms of local input-to-output computer system latency. In this paper, we review this prior art and provide an additional case study with data demonstrating the importance of local system latency improvement, even at latency values below 20 ms. Though other factors may determine victory in a particular esports challenge, ensuring balanced local computer latency among competitors is essential to fair competition.

Aditya Jonnalagadda, Iuri Frosio, Seth Schneider et al.

Game publishers and anti-cheat companies have been unsuccessful in blocking cheating in online gaming. We propose a novel, vision-based approach that captures the final state of the frame buffer and detects illicit overlays. To this aim, we train and evaluate a DNN detector on a new dataset, collected using two first-person shooter games and three cheating software. We study the advantages and disadvantages of different DNN architectures operating on a local or global scale. We use output confidence analysis to avoid unreliable detections and inform when network retraining is required. In an ablation study, we show how to use Interval Bound Propagation to build a detector that is also resistant to potential adversarial attacks and study its interaction with confidence analysis. Our results show that robust and effective anti-cheating through machine learning is practically feasible and can be used to guarantee fair play in online gaming.