arXiv subjects

Phillip Guan

Publications and source records attributed to Phillip Guan.

8 recordsLinked to original sources

arXiv2026

Perceptual Requirements for Low-Latency Head-Mounted Displays

End-to-end (e2e) latency in head-mounted displays (HMD) is the time delay between a physical change in the world (e.g., a user's head movement) and the moment the display updates to reflect that change. Tracking, rendering, and other computation in real systems invariably introduce some amount of e2e latency to all HMDs. In modern devices this latency is usually in the range of 12-60 milliseconds which is partially addressed through pose prediction and late stage reprojection which means that perceptual studies and user experience evaluations cannot explore latencies below these values. Here, we introduce a video passthrough HMD, called Camsicle, which is capable of 2-millisecond e2e latency and, additionally, uses a catadioptric design to achieve perspective-correct passthrough without reprojection. This platform enables naturalistic user studies to interrogate the impacts of latency on user experience, preference, and performance. Across two user studies and 57 participants we find that 2 and 14.3 millisecond latencies are preferred over 23 and 29 milliseconds when attempting to catch a ball. Additionally, we compare individual latency preferences in this naturalistic ball-catching task to psychophysical thresholds for latency detection in a reference-grade system with zero latency to investigate how psychophysical thresholds may relate to subjective evaluations in naturalistic scenarios.

cs.GR↗

arXiv2025

Perceptual Sensitivity to Stereo Geometry Errors in Head-Mounted Displays

Stereoscopic head-mounted displays (HMDs) render and present binocular images to create an egocentric, 3D percept to the HMD user. Within this render and presentation pipeline there are potential rendering camera and viewing position errors that can induce deviations in the depth and distance that a user perceives compared to the underlying intended geometry. For example, rendering errors can arise when HMD render cameras are incorrectly positioned relative to the assumed centers of projections of the HMD displays and viewing errors can arise when users view stereo geometry from the incorrect location in the HMD eyebox. In this work we present a geometric framework that predicts errors in distance perception arising from inaccurate HMD perspective geometry and build an HMD platform to reliably simulate render and viewing error in a Quest 3 HMD with eye tracking to experimentally test these predictions. We present a series of five experiments to explore the efficacy of this geometric framework and show that errors in perspective geometry can induce both under- and over-estimations in perceived distance. We further demonstrate how real-time visual feedback can be used to dynamically recalibrate visuomotor mapping so that an accurate reach distance is achieved even if the perceived visual distance is negatively impacted by geometric error.

cs.HC↗

arXiv2025

Subthreshold Jitter in VR Can Induce Visual Discomfort

Visual-vestibular conflicts (VVCs) are a primary contributor to visually induced motion sickness (VIMS) in head-mounted displays (HMDs). However, virtual reality (VR) comfort studies often rely on exposing seated or standing users to experiences with high intensity visual motion (such as roller coasters). These drastic VVCs tend to induce pronounced VIMS symptoms that can be reliably detected across individuals using common survey measures. The conclusions from studies using these extreme motion-based conflicts may not accurately generalize to naturalistic use cases in VR where efforts are made to minimize, rather than maximize, VIMS symptoms. In this work, we show that a subthreshold visual-vestibular conflict can induce measurable discomfort during naturalistic, long duration use. We first present a psychophysical study, conducted outside of an HMD, to rigorously identify the perceptual thresholds for sinusoidal noise in render pose (i.e., jitter) resulting in erroneous 3D motion of rendered content. We next introduce subthreshold levels of jitter to a Meta Quest 3 VR HMD and demonstrate that this can induce visual discomfort in participants playing the commercially-available game Cubism across a three-session, repeated-measures study. Importantly, we did not identify statistically significant comfort differences between control and jitter conditions with traditional pre- and post-test comparison of Simulator Sickness Questionnaire (SSQ) scores. Significant differences were only identified using the Motion Illness Symptoms Classification (MISC) survey administered every 10 minutes across each 90 minute session. This highlights the benefits of incorporating time-resolved data points and suggests that lightweight, more frequent surveys may be important tools for measuring visual discomfort in more ecologically-valid scenarios.

cs.HC↗

arXiv2025

Mixed Likelihood Variational Gaussian Processes

Gaussian processes (GPs) are powerful models for human-in-the-loop experiments due to their flexibility and well-calibrated uncertainty. However, GPs modeling human responses typically ignore auxiliary information, including a priori domain expertise and non-task performance information like user confidence ratings. We propose mixed likelihood variational GPs to leverage auxiliary information, which combine multiple likelihoods in a single evidence lower bound to model multiple types of data. We demonstrate the benefits of mixing likelihoods in three real-world experiments with human participants. First, we use mixed likelihood training to impose prior knowledge constraints in GP classifiers, which accelerates active learning in a visual perception task where users are asked to identify geometric errors resulting from camera position errors in virtual reality. Second, we show that leveraging Likert scale confidence ratings by mixed likelihood training improves model fitting for haptic perception of surface roughness. Lastly, we show that Likert scale confidence ratings improve human preference learning in robot gait optimization. The modeling performance improvements found using our framework across this diverse set of applications illustrates the benefits of incorporating auxiliary information into active learning and preference learning by using mixed likelihoods to jointly model multiple inputs.

cs.LG↗

arXiv2024

Perceptual Thresholds for Radial Optic Flow Distortion in Near-Eye Stereoscopic Displays

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 HMDs 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, ~0.15% 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/.

cs.HC↗

arXiv2024

Saccade-Contingent Rendering

Battery-constrained power consumption, compute limitations, and high frame rate requirements in head-mounted displays present unique challenges in the drive to present increasingly immersive and comfortable imagery in virtual reality. However, humans are not equally sensitive to all regions of the visual field, and perceptually-optimized rendering techniques are increasingly utilized to address these bottlenecks. Many of these techniques are gaze-contingent and often render reduced detail away from a user's fixation. Such techniques are dependent on spatio-temporally-accurate gaze tracking and can result in obvious visual artifacts when eye tracking is inaccurate. In this work we present a gaze-contingent rendering technique which only requires saccade detection, bypassing the need for highly-accurate eye tracking. In our first experiment, we show that visual acuity is reduced for several hundred milliseconds after a saccade. In our second experiment, we use these results to reduce the rendered image resolution after saccades in a controlled psychophysical setup, and find that observers cannot discriminate between saccade-contingent reduced-resolution rendering and full-resolution rendering. Finally, in our third experiment, we introduce a 90 pixels per degree headset and validate our saccade-contingent rendering method under typical VR viewing conditions.

cs.GR↗

arXiv2023

Perceptual Requirements for World-Locked Rendering in AR and VR

Stereoscopic, head-tracked display systems can show users realistic, world-locked virtual objects and environments. However, discrepancies between the rendering pipeline and physical viewing conditions can lead to perceived instability in the rendered content resulting in reduced immersion and, potentially, visually-induced motion sickness. Precise requirements to achieve perceptually stable world-locked rendering (WLR) are unknown due to the challenge of constructing a wide field of view, distortion-free display with highly accurate head and eye tracking. We present a system capable of rendering virtual objects over real-world references without perceivable drift under such constraints. This platform is used to study acceptable errors in render camera position for WLR in augmented and virtual reality scenarios, where we find an order of magnitude difference in perceptual sensitivity. We conclude with an analytic model which examines changes to apparent depth and visual direction in response to camera displacement errors.

cs.GR↗

arXiv2022

Look-Ahead Acquisition Functions for Bernoulli Level Set Estimation

Level set estimation (LSE) is the problem of identifying regions where an unknown function takes values above or below a specified threshold. Active sampling strategies for efficient LSE have primarily been studied in continuous-valued functions. Motivated by applications in human psychophysics where common experimental designs produce binary responses, we study LSE active sampling with Bernoulli outcomes. With Gaussian process classification surrogate models, the look-ahead model posteriors used by state-of-the-art continuous-output methods are intractable. However, we derive analytic expressions for look-ahead posteriors of sublevel set membership, and show how these lead to analytic expressions for a class of look-ahead LSE acquisition functions, including information-based methods. Benchmark experiments show the importance of considering the global look-ahead impact on the entire posterior. We demonstrate a clear benefit to using this new class of acquisition functions on benchmark problems, and on a challenging real-world task of estimating a high-dimensional contrast sensitivity function.

stat.ML↗