Gamers use a variety of different display sizes, though for PC gaming, monitors in the 24 to 27 inch size range have become most popular.Particularly popular among many PC gamers, first person shooter (FPS) games represent a genre where hand-eye coordination is particularly central to the player's performance in game.In a carefully designed set of experiments on FPS aiming, we compare player performance across a range of display sizes.In two experiments, we compare 12.5 inch, 17.3 inch and 24 inch monitors on a multi-target elimination task, once with stationary, and once with moving targets.We find that aiming improves as display sizes increase.Next, we highlight the differences between 24.5 inch and 27 inch displays in a third experiment using very small targets.We find a small, but statistically significant improvement in aiming when using the larger monitor.Overall, our results indicate that in typical desktop gaming settings with freely varying head position and field of view, FPS aiming improves as display size grows, though improvements begin to decline as size approaches 30 inches.

Computer games - and computer game players - often drive technology improvements, with graphics cards and monitors pushing the limits of display technologies. High frame rates, in particular, promise to provide lower latencies and smoother game visuals to gamers, especially important for competitive first person shooter (FPS) game players. What is not well-known is to what extent gamers benefit from ultra-high frame rates in terms of player performance and quality of experience. This paper studies the effects of frame rates - especially high frame rates - on FPS game players. A custom FPS game was developed to allow for consistent delivery of frame rates from 7 f/s to 500 f/s, while recording objective (performance) and subjective (smoothness) measures. Analysis of data from a 44-person user study shows player performance (e.g., score) improves sharply from 7+ f/s, but levels out after about 90 f/s. However, users perception benefits over the full range of frame rates studied, rising sharply from 7+ f/s, but continuing to improve through the top 500 f/s.

Frametime spikes can disrupt gameplay in first-person shooter (FPS) games, affecting both performance and player experience. This paper examines how spikes during specific game events impact players. We developed a custom FPS game that maintains a steady 500 frames/s while inducing frametime spikes during weapon reloading, fast mouse movement, or targeting. Thirty-eight (38) participants played the game in a user study, providing both performance data and user-reported visual smoothness. Results show that spikes while targeting lowered accuracy and score, while spikes during reloads and mouse movement did not affect performance but still degraded user experience. These results suggest that both the relative timing and size of frametime spikes matter in FPS gameplay. Per-action models better account for average QoE when spikes happen, showing better fit than models that only consider spike size (independent of action).

User studies are a cornerstone of human-computer interaction research, including measures of user performance and quality of experience (QoE) – particularly important for games where frame rates and frame timings can impact performance. Unfortunately, commercial games have limited options for customization and do not log player performance data with sufficient detail for use in such studies. This paper introduces Lead Rush, a first-person shooter game designed for conducting user studies on the effects of frame timing and frame rate. Lead Rush is tuned to run at extremely high frame rates and includes hooks to induce frame time "spikes". Researchers can configure Lead Rush’s gameplay, trigger frame time spikes during specific actions, and log player data per game round and per study session. This paper also introduces a dataset from a user study on the effects of frame time spikes on player performance gathered from Lead Rush, and includes gameplay logs with both performance data and QoE results. Some analysis of the dataset is presented to illustrate its use.

With the advent of variable refresh rate (VRR) monitor technologies, gamers experience variable frame timing (VFT) during their gameplay. Combining VRR with low-latency GPU rendering and increased display refresh rates enables smoother variation of frame presentation sequences. Here, we assess how VFT affects self-reported perceived smoothness of game play by introducing frequent but relatively small ($\mathbf{(4 - 1 2 ~ m s}$) variations in frame time around typical refresh rates (30-240 Hz). Our results demonstrate that VFT degrades the perceived smoothness of game play for large variation in frame time (12 ms) but has a diminished effect on perceived smoothness for small variations in frame time (4 ms).

In first-person shooter (FPS) games, the peeker’s advantage is the edge the moving peeker gets when battling a stationary defender at a corner due to network latency. However, confirmation of (the size of) this advantage based on network latency and the distance from the corner has not been studied. This paper assesses the peeker’s advantage via two user studies both using an open-source FPS game extended to support two-player networking and a custom map. Users play as both peeker and defender with 3 different corner distances and 3 different network latencies. Analysis of hits, wins, and time-to-damage shows that the advantage for the peeker is impacted more by the defender’s latency than the peeker’s latency and is lowest when the peeker is nearest the corner. The user study with a tournament setting had quicker and more competitive matches resulting in more combat encounters and closer games than did the traditional user study.

Abstract 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.

Mouse sensitivity in first-person targeting tasks is a highly debated issue. Recommendations within a single game can vary by a factor of 10× or more and are an active topic of experimentation in both competitive and recreational esports communities. Inspired by work in pointer-based gain optimization and extending our previous results from the first user study focused on mouse sensitivity in first-person targeting tasks (Boudaoud et al., 2023), we describe a range of optimal mouse sensitivity wherein players perform statistically significantly better in task completion time and throughput. For tasks involving first-person view control, mouse sensitivity is best described using the ratio between an in-game rotation of the view and corresponding physical displacement of the mouse. We discuss how this displacement-to-rotation sensitivity is incompatible with the control-display gain reported in traditional pointer-based gain studies as well as other rotational gains reported in head-controlled interface studies. We provide additional details regarding impacts of mouse dots per inch, on reported sensitivity, the distribution of spatial difficulty in our experiment, our submovement parsing algorithm, and relationships between measured parameters, further demonstrating optimal sensitivity arising from a speed-precision tradeoff. We conclude our work by updating and improving our suggestions for mouse sensitivity selection and refining directions for future work.

Despite billions of hours of play and copious discussion online, mouse sensitivity recommendations for first-person targeting tasks vary by a factor of 10x or more and remain an active topic of debate in both competitive and recreational gaming communities. Inspired by previous academic literature in pointer-based gain optimization, we conduct the first user study of mouse sensitivity in first person targeting tasks, reporting a statistically significant range of optimal values in both task completion time and throughput. Due to inherent incompatibility (i.e., lack of convert-ability) between sensitivity metrics adopted for prior pointer-based gain literature and those describing first-person targeting, we provide the first analytically demonstrated, statistically significant optimal sensitivity range useful for first-person camera controls. Furthermore, we demonstrate that this optimal sensitivity range arises (at least in part) from a speed-precision trade-off impacted by spatial task difficulty, similar to results reported in pointer-based sensitivity literature previously.

Which display size helps gamers win? Recommendations from the research and PC gaming communities are contradictory. We find that as display size grows, targeting performance improves. When size increases from 13′′ to 26′′, targeting time drops by over 3%. Further size increases from 26′′ through 39′′, 52′′ and 65′′, bring more modest improvements, with targeting time dropping a further 1%. While such improvements may not be meaningful for novice gamers, they are extremely important to skilled and competitive players. To produce these results, 30 gamers participated in a targeting task as we varied display size by placing a display at varying distances. We held field of view constant by varying viewport size, and resolution constant by rendering to a fixed-size off-screen buffer. This paper offers further experimental detail, and examines likely explanations for the effects of display size.

Quality, diversity, and size of training data are critical factors for learning-based gaze estimators. We create two datasets satisfying these criteria for near-eye gaze estimation under infrared illumination: a synthetic dataset using anatomically-informed eye and face models with variations in face shape, gaze direction, pupil and iris, skin tone, and external conditions (2M images at 1280x960), and a real-world dataset collected with 35 subjects (2.5M images at 640x480). Using these datasets we train neural networks performing with sub-millisecond latency. Our gaze estimation network achieves 2.06(±0.44)° of accuracy across a wide 30°×40° field of view on real subjects excluded from training and 0.5° best-case accuracy (across the same FOV) when explicitly trained for one real subject. We also train a pupil localization network which achieves higher robustness than previous methods.

We present a near-eye augmented reality display with resolution and focal depth dynamically driven by gaze tracking. The display combines a traveling microdisplay relayed off a concave half-mirror magnifier for the high-resolution foveal region, with a wide field-of-view peripheral display using a projector-based Maxwellian-view display whose nodal point is translated to follow the viewer's pupil during eye movements using a traveling holographic optical element. The same optics relay an image of the eye to an infrared camera used for gaze tracking, which in turn drives the foveal display location and peripheral nodal point. Our display supports accommodation cues by varying the focal depth of the microdisplay in the foveal region, and by rendering simulated defocus on the "always in focus" scanning laser projector used for peripheral display. The resulting family of displays significantly improves on the field-of-view, resolution, and form-factor tradeoff present in previous augmented reality designs. We show prototypes supporting 30, 40 and 60 cpd foveal resolution at a net 85° × 78° field of view per eye.

Foveated rendering is a performance optimization based on the well-known degradation of peripheral visual acuity. It reduces computational costs by showing a high-quality image in the user’s central (foveal) vision and a lower quality image in the periphery. Foveated rendering is a promising optimization for Virtual Reality (VR) graphics, and generally requires accurate and low-latency eye tracking to ensure correctness even when a user makes large, fast eye movements such as saccades. However, due to the phenomenon of saccadic omission, it is possible that these requirements may be relaxed. In this article, we explore the effect of latency for foveated rendering in VR applications. We evaluated the detectability of visual artifacts for three techniques capable of generating foveated images and for three different radii of the high-quality foveal region. Our results show that larger foveal regions allow for more aggressive foveation, but this effect is more pronounced for temporally stable foveation techniques. Added eye tracking latency of 80--150ms causes a significant reduction in acceptable amount of foveation, but a similar decrease in acceptable foveation was not found for shorter eye-tracking latencies of 20--40ms, suggesting that a total system latency of 50--70ms could be tolerated.

A variety of applications such as virtual reality and immersive cinema require high image quality, low rendering latency, and consistent depth cues. 4D light field displays support focus accommodation, but are more costly to render than 2D images, resulting in higher latency. The human visual system can resolve higher spatial frequencies in the fovea than in the periphery. This property has been harnessed by recent 2D foveated rendering methods to reduce computation cost while maintaining perceptual quality. Inspired by this, we present foveated 4D light fields by investigating their effects on 3D depth perception. Based on our psychophysical experiments and theoretical analysis on visual and display bandwidths, we formulate a content-adaptive importance model in the 4D ray space. We verify our method by building a prototype light field display that can render only 16% -- 30% rays without compromising perceptual quality.

Foveated rendering synthesizes images with progressively less detail outside the eye fixation region, potentially unlocking significant speedups for wide field-of-view displays, such as head mounted displays, where target framerate and resolution is increasing faster than the performance of traditional real-time renderers. To study and improve potential gains, we designed a foveated rendering user study to evaluate the perceptual abilities of human peripheral vision when viewing today's displays. We determined that filtering peripheral regions reduces contrast, inducing a sense of tunnel vision. When applying a postprocess contrast enhancement, subjects tolerated up to 2× larger blur radius before detecting differences from a non-foveated ground truth. After verifying these insights on both desktop and head mounted displays augmented with high-speed gaze-tracking, we designed a perceptual target image to strive for when engineering a production foveated renderer. Given our perceptual target, we designed a practical foveated rendering system that reduces number of shades by up to 70% and allows coarsened shading up to 30° closer to the fovea than Guenter et al. [2012] without introducing perceivable aliasing or blur. We filter both pre- and post-shading to address aliasing from undersampling in the periphery, introduce a novel multiresolution- and saccade-aware temporal antialising algorithm, and use contrast enhancement to help recover peripheral details that are resolvable by our eye but degraded by filtering. We validate our system by performing another user study. Frequency analysis shows our system closely matches our perceptual target. Measurements of temporal stability show we obtain quality similar to temporally filtered non-foveated renderings.

We are motivated by the problem of designing a simple distributed algorithm for peer-to-peer streaming applications that can achieve high throughput and low delay, while allowing the neighbor set maintained by each peer to be small. While previous works have mostly used tree structures, our algorithm constructs multiple random directed Hamiltonian cycles and disseminates content over the superposed graph of the cycles. We show that it is possible to achieve the maximum streaming capacity even when each peer only transmits to and receives from Θ(1) neighbors. Further, we show that the proposed algorithm achieves the streaming delay of Θ(log N) when the streaming rate is less than (1 - 1/K) of the maximum capacity for any fixed constant K ≥ 2, where N denotes the number of peers in the network. The key theoretical contribution is to characterize the distance between peers in a graph formed by the superposition of directed random Hamiltonian cycles, in which edges from one of the cycles may be dropped at random. We use Doob martingales and graph expansion ideas to characterize this distance as a function of N, with high probability.

We consider structured peer-to-peer (P2P) networks for distributing streaming data such as real-time video. In such P2P networks, each chunk of data is transferred from the server to all the peers using a data distribution tree. The number of trees and the number of children in each tree contribute to the overhead in the data distribution process. In this paper, we show that the maximum streaming rate can be achieved using O(logN) trees in a network of N peers with homogeneous upload capacities, where each peer has O(1) children in each tree. It is further shown that O(1) trees suffice to achieve a near- maximum streaming rate with heterogeneous upload capacities. The solution involves mapping the tree construction problem to a novel Block Packing problem where two-dimensional blocks are packed into a two-dimensional bin subject to some packing constraints. The block packing problem allows us to visualize network bandwidth usage, thus facilitating a particular way to construct trees which establish the above bounds.

In wireless sensor networks (WSNs), asynchronous sleep-wake scheduling protocols can be used to significantly reduce energy consumption without incurring the communication overhead for clock synchronization needed for synchronous sleep-wake scheduling protocols. However, these savings could come at a significant cost in delay performance. Recently, researchers have attempted to exploit the inherent broadcast nature of the wireless medium to reduce this delay with virtually no additional energy cost. These schemes are called “anycasting,” where each sensor node forwards the packet to the first node that wakes up among a set of candidate next-hop nodes. In this paper, we develop a delay-optimal anycasting scheme under periodic sleep-wake patterns. Our solution is computationally simple and fully distributed. Furthermore, we show that periodic sleep-wake patterns result in the smallest delay among all wake-up patterns under given energy constraints. Simulation results illustrate the benefit of our proposed schemes over the state of the art.

In wireless sensor networks, asynchronous sleep-wake scheduling protocols can significantly reduce energy consumption without incurring the communication overhead for clock synchronization used in typical sleep-wake scheduling protocols. However, the savings could come at a significant cost in delay performance. Recently, researchers have attempted to exploit the inherent broadcast nature of the wireless medium to reduce this delay with virtually no additional energy cost. These schemes are called "anycasting," where each sensor node forwards the packet to the first node that wakes up among a set of candidate next-hop nodes. In this paper, we develop a delay-optimal anycasting scheme under periodic sleep-wake patterns. Our solution is computationally simple and fully distributed. We show that periodic sleep-wake patterns result in the smallest delay among all wake-up patterns under given energy constraints. Simulation results illustrate the benefit of our proposed schemes over the state-of-the art.

Sleep-wake scheduling is an effective mechanism to prolong the lifetime of energy-constrained wireless sensor networks. However, it incurs an additional delay for packet delivery when each node needs to wait for its next-hop relay node to wake up, which could be unacceptable for delay-sensitive applications. Prior work in the literature has proposed to reduce this delay using anycast, where each node opportunistically selects the first neighboring node that wakes up among multiple candidate nodes. In this paper, we study the joint control problem of how to optimally control the sleep-wake schedule, the anycast candidate set of next-hop neighbors, and anycast priorities, to maximize the network lifetime subject to a constraint on the expected end-to-end delay. We provide an efficient solution to this joint control problem. Our numerical results indicate that the proposed solution can substantially outperform prior heuristic solutions in the literature, especially under the practical scenarios where there are obstructions in the coverage area of the wireless sensor network.