Display technologies in the fields of virtual and augmented reality affect the appearance of human representations, such as avatars used in telepresence or entertainment applications, based on the user’s current viewing conditions. With changing viewing conditions, it is possible that the perceived appearance of one’s avatar changes in an unexpected or undesired manner, which may change user behavior towards these avatars and cause frustration in using the AR display. In this paper, we describe a user study (N=20) where participants saw themselves in a mirror standing next to their own avatar through use of a HoloLens 2 optical see-through head-mounted display. Participants were tasked to match their avatar’s appearance to their own under two environment lighting conditions (200 lux and 2,000 lux). Our results showed that the intensity of environment lighting had a significant effect on participants selected skin colors for their avatars, where participants with dark skin colors tended to make their avatar’s skin color lighter, nearly to the level of participants with light skin color. Further, in particular female participants made their avatar’s hair color darker for the lighter environment lighting condition. We discuss our results with a view on technological limitations and effects on the diversity of avatar representations on optical see-through displays.
more »
« less
An Extended Analysis on the Benefits of Dark Mode User Interfaces in Optical See-Through Head-Mounted Displays
Light-on-dark color schemes, so-called “Dark Mode,” are becoming more and more popular over a wide range of display technologies and application fields. Many people who have to look at computer screens for hours at a time, such as computer programmers and computer graphics artists, indicate a preference for switching colors on a computer screen from dark text on a light background to light text on a dark background due to perceived advantages related to visual comfort and acuity, specifically when working in low-light environments. In this article, we investigate the effects of dark mode color schemes in the field of optical see-through head-mounted displays (OST-HMDs), where the characteristic “additive” light model implies that bright graphics are visible but dark graphics are transparent . We describe two human-subject studies in which we evaluated a normal and inverted color mode in front of different physical backgrounds and different lighting conditions. Our results indicate that dark mode graphics displayed on the HoloLens have significant benefits for visual acuity and usability, while user preferences depend largely on the lighting in the physical environment. We discuss the implications of these effects on user interfaces and applications.
more »
« less
- Award ID(s):
- 1800961
- PAR ID:
- 10275736
- Date Published:
- Journal Name:
- ACM Transactions on Applied Perception
- Volume:
- 18
- Issue:
- 3
- ISSN:
- 1544-3558
- Page Range / eLocation ID:
- 1 to 22
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
more » « less
Abstract Looking at a cup of hot tea, an observer can see color patterns and granular textures both on the water surface and in the steam. Motivated by this example, we model the appearance of iridescent water droplets. Mie scattering describes the scattering of light waves by individual spherical particles and is the building block for both effects, but we show that other mechanisms must also be considered in order to faithfully reproduce the appearance. Iridescence on the water surface is caused by droplets levitating above the surface, and interference between light scattered by drops and reflected by the water surface, known as Quetelet scattering, is essential to producing the color. We propose a model, new to computer graphics, for rendering this phenomenon, which we validate against photographs. For iridescent steam, we show that variation in droplet size is essential to the characteristic color patterns. We build a droplet growth model and apply it as a post‐processing step to an existing computer graphics fluid simulation to compute collections of particles for rendering. We significantly accelerate the rendering of sparse particles with motion blur by intersecting rays with particle trajectories, blending contributions along viewing rays. Our model reproduces the distinctive color patterns correlated with the steam flow. For both effects, we instantiate individual droplets and render them explicitly, since the granularity of droplets is readily observed in reality, and demonstrate that Mie scattering alone cannot reproduce the visual appearance.
-
null (Ed.)Due to the additive light model employed by most optical see-through head-mounted displays (OST-HMDs), they provide the best augmented reality (AR) views in dark environments, where the added AR light does not have to compete against existing real-world lighting. AR imagery displayed on such devices loses a significant amount of contrast in well-lit environments such as outdoors in direct sunlight. To compensate for this, OST-HMDs often use a tinted visor to reduce the amount of environment light that reaches the user’s eyes, which in turn results in a loss of contrast in the user’s physical environment. While these effects are well known and grounded in existing literature, formal measurements of the illuminance and contrast of modern OST-HMDs are currently missing. In this paper, we provide illuminance measurements for both the Microsoft HoloLens 1 and its successor the HoloLens 2 under varying environment lighting conditions ranging from 0 to 20,000 lux. We evaluate how environment lighting impacts the user by calculating contrast ratios between rendered black (transparent) and white imagery displayed under these conditions, and evaluate how the intensity of environment lighting is impacted by donning and using the HMD. Our results indicate the further need for refinement in the design of future OST-HMDs to optimize contrast in environments with illuminance values greater than or equal to those found in indoor working environments.more » « less
-
more » « less
Abstract Efficient rendering of photo‐realistic virtual worlds is a long standing effort of computer graphics. Modern graphics techniques have succeeded in synthesizing photo‐realistic images from hand‐crafted scene representations. However, the automatic generation of shape, materials, lighting, and other aspects of scenes remains a challenging problem that, if solved, would make photo‐realistic computer graphics more widely accessible. Concurrently, progress in computer vision and machine learning have given rise to a new approach to image synthesis and editing, namely deep generative models. Neural rendering is a new and rapidly emerging field that combines generative machine learning techniques with physical knowledge from computer graphics, e.g., by the integration of differentiable rendering into network training. With a plethora of applications in computer graphics and vision, neural rendering is poised to become a new area in the graphics community, yet no survey of this emerging field exists. This state‐of‐the‐art report summarizes the recent trends and applications of neural rendering. We focus on approaches that combine classic computer graphics techniques with deep generative models to obtain controllable and photorealistic outputs. Starting with an overview of the underlying computer graphics and machine learning concepts, we discuss critical aspects of neural rendering approaches. Specifically, our emphasis is on the type of control, i.e., how the control is provided, which parts of the pipeline are learned, explicit vs. implicit control, generalization, and stochastic vs. deterministic synthesis. The second half of this state‐of‐the‐art report is focused on the many important use cases for the described algorithms such as novel view synthesis, semantic photo manipulation, facial and body reenactment, relighting, free‐viewpoint video, and the creation of photo‐realistic avatars for virtual and augmented reality telepresence. Finally, we conclude with a discussion of the social implications of such technology and investigate open research problems.
-
An accurate understanding of omnidirectional environment lighting is crucial for high-quality virtual object rendering in mobile augmented reality (AR). In particular, to support reflective rendering, existing methods have leveraged deep learning models to estimate or have used physical light probes to capture physical lighting, typically represented in the form of an environment map. However, these methods often fail to provide visually coherent details or require additional setups. For example, the commercial framework ARKit uses a convolutional neural network that can generate realistic environment maps; however the corresponding reflective rendering might not match the physical environments. In this work, we present the design and implementation of a lighting reconstruction framework called LITAR that enables realistic and visually-coherent rendering. LITAR addresses several challenges of supporting lighting information for mobile AR. First, to address the spatial variance problem, LITAR uses two-field lighting reconstruction to divide the lighting reconstruction task into the spatial variance-aware near-field reconstruction and the directional-aware far-field reconstruction. The corresponding environment map allows reflective rendering with correct color tones. Second, LITAR uses two noise-tolerant data capturing policies to ensure data quality, namely guided bootstrapped movement and motion-based automatic capturing. Third, to handle the mismatch between the mobile computation capability and the high computation requirement of lighting reconstruction, LITAR employs two novel real-time environment map rendering techniques called multi-resolution projection and anchor extrapolation. These two techniques effectively remove the need of time-consuming mesh reconstruction while maintaining visual quality. Lastly, LITAR provides several knobs to facilitate mobile AR application developers making quality and performance trade-offs in lighting reconstruction. We evaluated the performance of LITAR using a small-scale testbed experiment and a controlled simulation. Our testbed-based evaluation shows that LITAR achieves more visually coherent rendering effects than ARKit. Our design of multi-resolution projection significantly reduces the time of point cloud projection from about 3 seconds to 14.6 milliseconds. Our simulation shows that LITAR, on average, achieves up to 44.1% higher PSNR value than a recent work Xihe on two complex objects with physically-based materials.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1145/3456874
