Video generated by the current state-of-the-art generative models contain undesirable artifacts. We introduce GeneVA, the first large-scale dataset of human-annotated artifact bounding boxes in AI-generated videos. The dataset consists of 16,356 AI-generated videos, each labeled by a human annotator with per-frame artifact bounding boxes, their labels and descriptions, and video quality ratings. A custom data collection pipeline was developed in Prolific, and a novel taxonomy for spatio-temporal artifacts present in AI-generated videos was defined. The videos were from the VidProM [41] dataset, with text prompts from this dataset then used to generate an additional subset of videos using Sora. We trained an artifact detector and caption generator using a pre-trained image-based model, and a custom temporal fusion module. The dataset can be found at https://www.immersivecomputinglab.org/publication/geneva. We hope that datasets like GeneVA will encourage improvements in artifact detection in AI-generated video towards applications such as deepfake detection.

In graphics applications featuring dynamically moving visual targets – such as film and gaming – we have to rotate our eyes to follow objects as they move across the screen. Because target motion is often unpredictable and ever-changing, we must rapidly respond to motion cues and adjust eye movements to maintain the target within the fovea, a process known as catch-up. This catch-up behavior reflects how efficiently the eyes react to and compensate for sudden changes in motion, making it a critical indicator for both task performance and the overall visual experience. In this work, we study and measure the eye catch-up performance during visual tracking. In particular, we present a behavioral analysis that predicts users’ reaction latency to abrupt target motion based on target visibility. Our numerical analysis and human subject studies evidence the effectiveness and generalizability. We further show how the catch-up metric can be applied to evaluate video quality, adjust game difficulty, and optimize display configurations for enhanced user performance. We envision this research to create a computational link between human perception and behavioral performance in dynamic graphics contexts.

We study the correlations between redirected walking (RDW) rotation gains and patterns in users' posture and gaze data during locomotion in virtual reality (VR). To do this, we conducted a psychophysical experiment to measure users' sensitivity to RDW rotation gains and collect gaze and posture data during the experiment. Using multilevel modeling, we studied how different factors of the VR system and user affected their physiological signals. In particular, we studied the effects of redirection gain, trial duration, trial number (i.e., time spent in VR), and participant gender on postural sway, gaze velocity (a proxy for gaze stability), and saccade and blink rate. Our results showed that, in general, physiological signals were significantly positively correlated with the strength of redirection gain, the duration of trials, and the trial number. Gaze velocity was negatively correlated with trial duration. Additionally, we measured users' sensitivity to rotation gains in well-lit (photopic) and dimly-lit (mesopic) virtual lighting conditions. Results showed that there were no significant differences in RDW detection thresholds between the photopic and mesopic luminance conditions.

We provide the first perceptual quantification of user's sensitivity to radial optic flow artifacts and demonstrate a promising approach for masking this optic flow artifact via blink suppression. Near-eye HMOs allow users to feel immersed in virtual environments by providing visual cues, like motion parallax and stereoscopy, that mimic how we view the physical world. However, these systems exhibit a variety of perceptual artifacts that can limit their usability and the user's sense of presence in VR. One well-known artifact is the vergence-accommodation conflict (VAC). Varifocal displays can mitigate VAC, but bring with them other artifacts such as a change in virtual image size (radial optic flow) when the focal plane changes. We conducted a set of psychophysical studies to measure users' ability to perceive this radial flow artifact before, during, and after self-initiated blinks. Our results showed that visual sensitivity was reduced by a factor of 10 at the start and for ~70 ms after a blink was detected. Pre- and post-blink sensitivity was, on average, ~O.15% image size change during normal viewing and increased to ~1.5- 2.0% during blinks. Our results imply that a rapid (under 70 ms) radial optic flow distortion can go unnoticed during a blink. Furthermore, our results provide empirical data that can be used to inform engineering requirements for both hardware design and software-based graphical correction algorithms for future varifocal near-eye displays. Our project website is available at https://gamma.umd.edu/ROF/.

Haptic feedback is an important component of creating an immersive mixed reality experience. Traditionally, haptic forces are rendered in response to the user's interactions with the virtual environment. In this work, we explore the idea of rendering haptic forces in a proactive manner, with the explicit intention to influence the user's behavior through compelling haptic forces. To this end, we present a framework for active haptic guidance in mixed reality, using one or more robotic haptic proxies to influence user behavior and deliver a safer and more immersive virtual experience. We provide details on common challenges that need to be overcome when implementing active haptic guidance, and discuss example applications that show how active haptic guidance can be used to influence the user's behavior. Finally, we apply active haptic guidance to a virtual reality navigation problem, and conduct a user study that demonstrates how active haptic guidance creates a safer and more immersive experience for users.

We present a novel metric to analyze the similarity between the physical environment and the virtual environment for natural walking in virtual reality. Our approach is general and can be applied to any pair of physical and virtual environments. We use geometric techniques based on conforming constrained Delaunay triangulations and visibility polygons to compute the Environment Navigation Incompatibility (ENI) metric that can be used to measure the complexity of performing simultaneous navigation. We demonstrate applications of ENI for highlighting regions of incompatibility for a pair of environments, guiding the design of the virtual environments to make them more compatible with a fixed physical environment, and evaluating the performance of different redirected walking controllers. We validate the ENI metric using simulations and two user studies. Results of our simulations and user studies show that in the environment pair that our metric identified as more navigable, users were able to walk for longer before colliding with objects in the physical environment. Overall, ENI is the first general metric that can automatically identify regions of high and low compatibility in physical and virtual environments. Our project website is available at https://gamma.umd.edu/eni/.

This paper introduces the PettingZoo library and the accompanying Agent Environment Cycle ("AEC") games model. PettingZoo is a library of diverse sets of multi-agent environments with a universal, elegant Python API. PettingZoo was developed with the goal of accelerating research in Multi-Agent Reinforcement Learning ("MARL"), by making work more interchangeable, accessible and reproducible akin to what OpenAI's Gym library did for single-agent reinforcement learning. PettingZoo's API, while inheriting many features of Gym, is unique amongst MARL APIs in that it's based around the novel AEC games model. We argue, in part through case studies on major problems in popular MARL environments, that the popular game models are poor conceptual models of the games commonly used with MARL, that they promote severe bugs that are hard to detect, and that the AEC games model addresses these problems.

We present a novel redirected walking controller based on alignment that allows the user to explore large and complex virtual environments, while minimizing the number of collisions with obstacles in the physical environment. Our alignment-based redirection controller, ARC, steers the user such that their proximity to obstacles in the physical environment matches the proximity to obstacles in the virtual environment as closely as possible. To quantify a controller's performance in complex environments, we introduce a new metric, Complexity Ratio (CR), to measure the relative environment complexity and characterize the difference in navigational complexity between the physical and virtual environments. Through extensive simulation-based experiments, we show that ARC significantly outperforms current state-of-the-art controllers in its ability to steer the user on a collision-free path. We also show through quantitative and qualitative measures of performance that our controller is robust in complex environments with many obstacles. Our method is applicable to arbitrary environments and operates without any user input or parameter tweaking, aside from the layout of the environments. We have implemented our algorithm on the Oculus Quest head-mounted display and evaluated its performance in environments with varying complexity. Our project website is available at https://ganuna.umd.edu/arc/.

We present a new approach for redirected walking in static and dynamic scenes that uses techniques from robot motion planning to compute the redirection gains that steer the user on collision-free paths in the physical space. Our first contribution is a mathematical framework for redirected walking using concepts from motion planning and configuration spaces. This framework highlights various geometric and perceptual constraints that tend to make collision-free redirected walking difficult. We use our framework to propose an efficient solution to the redirection problem that uses the notion of visibility polygons to compute the free spaces in the physical environment and the virtual environment. The visibility polygon provides a concise representation of the entire space that is visible, and therefore walkable, to the user from their position within an environment. Using this representation of walkable space, we apply redirected walking to steer the user to regions of the visibility polygon in the physical environment that closely match the region that the user occupies in the visibility polygon in the virtual environment. We show that our algorithm is able to steer the user along paths that result in significantly fewer resets than existing state-of-the-art algorithms in both static and dynamic scenes. Our project website is available at https://ganuna.umd.edu/vis.poly/.

We present a novel autoregression network to generate virtual agents that convey various emotions through their walking styles or gaits. Given the 3D pose sequences of a gait, our network extracts pertinent movement features and affective features from the gait. We use these features to synthesize subsequent gaits such that the virtual agents can express and transition between emotions represented as combinations of happy, sad, angry, and neutral. We incorporate multiple regularizations in the training of our network to simultaneously enforce plausible movements and noticeable emotions on the virtual agents. We also integrate our approach with an AR environment using a Microsoft HoloLens and can generate emotive gaits at interactive rates to increase the social presence. We evaluate how human observers perceive both the naturalness and the emotions from the generated gaits of the virtual agents in a web-based study. Our results indicate around 89% of the users found the naturalness of the gaits satisfactory on a five-point Likert scale, and the emotions they perceived from the virtual agents are statistically similar to the intended emotions of the virtual agents. We also use our network to augment existing gait datasets with emotive gaits and will release this augmented dataset for future research in emotion prediction and emotive gait synthesis. Our project website is available at https://gamma.umd.edu/gen-emotive-gaits/.

Redirected walking techniques enable users to naturally locomote in virtual environments (VEs) that are larger than the tracked space. Redirected walking imperceptibly transforms the VE around the user with predefined estimated threshold gains. Previously estimated gains were evaluated with a 40° field of view (FOV). We conducted a within-participant user study to estimate and compare thresholds for rotation gains. Significant differences in detection thresholds were found between FOVs. When using a 110° FOV, rotations can be decreased 31% and increased 49% compared to decreased 18% and increased 47% with a 40° FOV. Significant differences were found between female and male gains with a 110° FOV.

Redirected walking techniques enable users to naturally locomote in virtual environments (VEs) that are larger than the tracked space. Redirected walking imperceptibly transforms the VE around the user with predefined estimated threshold gains. Previously estimated gains were evaluated with a 40° field of view (FOV), and have not been evaluated in the presence of a distractor-a moving object in the VE that may capture the user's attention. We conducted a 2 (FOV: 40°, 110°) × 2 (Gender: female, male) × 2 (Distractor: without, with) user study to estimate and compare thresholds for rotation gains. Significant differences in detection thresholds were found between FOVs, and significant differences were found between female and male gains with a 110° FOV. Males had significantly wider gains using a 110° FOV compared to a 40° FOV, and distractors affected females differently than males. Finally, strong correlations were found between simulator sickness scores and threshold gains.