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Although emerging virtual and augmented reality (VR/AR) systems can produce highly immersive experiences, they can also cause visual discomfort, eyestrain, and nausea. One of the sources of these symptoms is a mismatch between vergence and focus cues. In current VR/AR near-eye displays, a stereoscopic image pair drives the vergence state of the human visual system to arbitrary distances, but the accommodation, or focus, state of the eyes is optically driven towards a fixed distance. In this work, we introduce a new display technology, dubbed accommodation-invariant (AI) near-eye displays, to improve the consistency of depth cues in near-eye displays. Rather than producing correct focus cues, AI displays are optically engineered to produce visual stimuli that are invariant to the accommodation state of the eye. The accommodation system can then be driven by stereoscopic cues, and the mismatch between vergence and accommodation state of the eyes is significantly reduced. We validate the principle of operation of AI displays using a prototype display that allows for the accommodation state of users to be measured while they view visual stimuli using multiple different display modes.

Emerging virtual reality (VR) displays must overcome the prevalent issue of visual discomfort to provide high-quality and immersive user experiences. In particular, the mismatch between vergence and accommodation cues inherent to most stereoscopic displays has been a long standing challenge. In this paper, we evaluate several adaptive display modes afforded by focus-tunable optics or actuated displays that have the promise to mitigate visual discomfort caused by the vergence-accommodation conflict, and improve performance in VR environments. We also explore monovision as an unconventional mode that allows each eye of an observer to accommodate to a different distance. While this technique is common practice in ophthalmology, we are the first to report its effectiveness for VR applications with a custom built set up. We demonstrate that monovision and other focus-tunable display modes can provide better user experiences and improve user performance in terms of reaction times and accuracy, particularly for nearby simulated distances in VR.

The field of visual computing is rapidly advancing due to the emergence of generative artificial intelligence (AI), which unlocks unprecedented capabilities for the generation, editing, and reconstruction of images, videos, and 3D scenes. In these domains, diffusion models are the generative AI architecture of choice. Within the last year alone, the literature on diffusion‐based tools and applications has seen exponential growth and relevant papers are published across the computer graphics, computer vision, and AI communities with new works appearing daily on arXiv. This rapid growth of the field makes it difficult to keep up with all recent developments. The goal of this state‐of‐the‐art report (STAR) is to introduce the basic mathematical concepts of diffusion models, implementation details and design choices of the popular Stable Diffusion model, as well as overview important aspects of these generative AI tools, including personalization, conditioning, inversion, among others. Moreover, we give a comprehensive overview of the rapidly growing literature on diffusion‐based generation and editing, categorized by the type of generated medium, including 2D images, videos, 3D objects, locomotion, and 4D scenes. Finally, we discuss available datasets, metrics, open challenges, and social implications. This STAR provides an intuitive starting point to explore this exciting topic for researchers, artists, and practitioners alike. [ABSTRACT FROM AUTHOR]

Proteins and other biomolecules form dynamic macromolecular machines that are tightly orchestrated to move, bind, and perform chemistry. Cryo-electron microscopy (cryo-EM) can access the intrinsic heterogeneity of these complexes and is therefore a key tool for understanding mechanism and function. However, 3D reconstruction of the resulting imaging data presents a challenging computational problem, especially without any starting information, a setting termed ab initio reconstruction. Here, we introduce a method, DRGN-AI, for ab initio heterogeneous cryo-EM reconstruction. With a two-step hybrid approach combining search and gradient-based optimization, DRGN-AI can reconstruct dynamic protein complexes from scratch without input poses or initial models. Using DRGN-AI, we reconstruct the compositional and conformational variability contained in a variety of benchmark datasets, process an unfiltered dataset of the DSL1/SNARE complex fully ab initio, and reveal a new "supercomplex" state of the human erythrocyte ankyrin-1 complex. With this expressive and scalable model for structure determination, we hope to unlock the full potential of cryo-EM as a high-throughput tool for structural biology and discovery.

Emerging spatial computing systems seamlessly superimpose digital information on the physical environment observed by a user, enabling transformative experiences across various domains, such as entertainment, education, communication and training 1-3 . However, the widespread adoption of augmented-reality (AR) displays has been limited due to the bulky projection optics of their light engines and their inability to accurately portray three-dimensional (3D) depth cues for virtual content, among other factors 4,5 . Here we introduce a holographic AR system that overcomes these challenges using a unique combination of inverse-designed full-colour metasurface gratings, a compact dispersion-compensating waveguide geometry and artificial-intelligence-driven holography algorithms. These elements are co-designed to eliminate the need for bulky collimation optics between the spatial light modulator and the waveguide and to present vibrant, full-colour, 3D AR content in a compact device form factor. To deliver unprecedented visual quality with our prototype, we develop an innovative image formation model that combines a physically accurate waveguide model with learned components that are automatically calibrated using camera feedback. Our unique co-design of a nanophotonic metasurface waveguide and artificial-intelligence-driven holographic algorithms represents a significant advancement in creating visually compelling 3D AR experiences in a compact wearable device., (© 2024. The Author(s).)

We propose a holographic projection system that achieves high image quality, brightness, and light efficiency. Using a novel, to the best of our knowledge, light-efficiency loss function, we are able to concentrate more light on the projection region and improve display brightness compared with conventional projectors. Leveraging emerging artificial intelligence-driven computer-generated holography and camera-in-the-loop calibration techniques, we learn a holographic wave propagation model using experimentally captured holographic images and demonstrate state-of-the-art light reallocation performance with high image quality.

Background: Linear accelerator (Linac) beam data commissioning and quality assurance (QA) play a vital role in accurate radiation treatment delivery and entail a large number of measurements using a variety of field sizes. How to optimize the effort in data acquisition while maintaining high quality of medical physics practice has been sought after., Purpose: We propose to model Linac beam data through implicit neural representation (NeRP) learning. The potential of the beam model in predicting beam data from sparse measurements and detecting data collection errors was evaluated, with the goal of using the beam model to verify beam data collection accuracy and simplify the commissioning and QA process., Materials and Methods: NeRP models with continuous and differentiable functions parameterized by multilayer perceptrons (MLPs) were used to represent various beam data including percentage depth dose (PDD) and profiles of 6 MV beams with and without flattening filter. Prior knowledge of the beam data was embedded into the MLP network by learning the NeRP of a vendor-provided "golden" beam dataset. The prior-embedded network was then trained to fit clinical beam data collected at one field size and used to predict beam data at other field sizes. We evaluated the prediction accuracy by comparing network-predicted beam data to water tank measurements collected from 14 clinical Linacs. Beam datasets with intentionally introduced errors were used to investigate the potential use of the NeRP model for beam data verification, by evaluating the model performance when trained with erroneous beam data samples., Results: Linac beam data predicted by the model agreed well with water tank measurements, with averaged Gamma passing rates (1%/1 mm passing criteria) higher than 95% and averaged mean absolute errors less than 0.6%. Beam data samples with measurement errors were revealed by inconsistent beam predictions between networks trained with correct versus erroneous data samples, characterized by a Gamma passing rate lower than 90%., Conclusion: A NeRP beam data modeling technique has been established for predicting beam characteristics from sparse measurements. The model provides a valuable tool to verify beam data collection accuracy and promises to simplify commissioning/QA processes by reducing the number of measurements without compromising the quality of medical physics service., (© 2023 American Association of Physicists in Medicine.)

This work introduces off-axis layered displays, the first approach to stereoscopic direct-view displays with support for focus cues. Off-axis layered displays combine a head-mounted display with a traditional direct-view display for encoding a focal stack and thus, for providing focus cues. To explore the novel display architecture, we present a complete processing pipeline for the real-time computation and post-render warping of off-axis display patterns. In addition, we build two prototypes using a head-mounted display in combination with a stereoscopic direct-view display, and a more widely available monoscopic direct-view display. In addition we show how extending off-axis layered displays with an attenuation layer and with eye-tracking can improve image quality. We thoroughly analyze each component in a technical evaluation and present examples captured through our prototypes.

Cryo-electron microscopy (cryo-EM) is an imaging modality that provides unique insights into the dynamics of proteins and other building blocks of life. The algorithmic challenge of jointly estimating the poses, 3D structure, and conformational heterogeneity of a biomolecule from millions of noisy and randomly oriented 2D projections in a computationally efficient manner, however, remains unsolved. Our method, cryoFIRE, performs ab initio heterogeneous reconstruction with unknown poses in an amortized framework, thereby avoiding the computationally expensive step of pose search while enabling the analysis of conformational heterogeneity. Poses and conformation are jointly estimated by an encoder while a physics-based decoder aggregates the images into an implicit neural representation of the conformational space. We show that our method can provide one order of magnitude speedup on datasets containing millions of images without any loss of accuracy. We validate that the joint estimation of poses and conformations can be amortized over the size of the dataset. For the first time, we prove that an amortized method can extract interpretable dynamic information from experimental datasets.

Cryo-electron microscopy (cryo-EM) has become a tool of fundamental importance in structural biology, helping us understand the basic building blocks of life. The algorithmic challenge of cryo-EM is to jointly estimate the unknown 3D poses and the 3D electron scattering potential of a biomolecule from millions of extremely noisy 2D images. Existing reconstruction algorithms, however, cannot easily keep pace with the rapidly growing size of cryo-EM datasets due to their high computational and memory cost. We introduce cryoAI, an ab initio reconstruction algorithm for homogeneous conformations that uses direct gradient-based optimization of particle poses and the electron scattering potential from single-particle cryo-EM data. CryoAI combines a learned encoder that predicts the poses of each particle image with a physics-based decoder to aggregate each particle image into an implicit representation of the scattering potential volume. This volume is stored in the Fourier domain for computational efficiency and leverages a modern coordinate network architecture for memory efficiency. Combined with a symmetrized loss function, this framework achieves results of a quality on par with state-of-the-art cryo-EM solvers for both simulated and experimental data, one order of magnitude faster for large datasets and with significantly lower memory requirements than existing methods.

This work introduces the first approach to video see-through mixed reality with full support for focus cues. By combining the flexibility to adjust the focus distance found in varifocal designs with the robustness to eye-tracking error found in multifocal designs, our novel display architecture reliably delivers focus cues over a large workspace. In particular, we introduce gaze-contingent layered displays and mixed reality focal stacks, an efficient representation of mixed reality content that lends itself to fast processing for driving layered displays in real time. We thoroughly evaluate this approach by building a complete end-to-end pipeline for capture, render, and display of focus cues in video see-through displays that uses only off-the-shelf hardware and compute components.

Understanding and modeling the dynamics of human gaze behavior in 360° environments is crucial for creating, improving, and developing emerging virtual reality applications. However, recruiting human observers and acquiring enough data to analyze their behavior when exploring virtual environments requires complex hardware and software setups, and can be time-consuming. Being able to generate virtual observers can help overcome this limitation, and thus stands as an open problem in this medium. Particularly, generative adversarial approaches could alleviate this challenge by generating a large number of scanpaths that reproduce human behavior when observing new scenes, essentially mimicking virtual observers. However, existing methods for scanpath generation do not adequately predict realistic scanpaths for 360° images. We present ScanGAN360, a new generative adversarial approach to address this problem. We propose a novel loss function based on dynamic time warping and tailor our network to the specifics of 360° images. The quality of our generated scanpaths outperforms competing approaches by a large margin, and is almost on par with the human baseline. ScanGAN360 allows fast simulation of large numbers of virtual observers, whose behavior mimics real users, enabling a better understanding of gaze behavior, facilitating experimentation, and aiding novel applications in virtual reality and beyond.

This Feature Issue includes 2 reviews and 34 research articles that highlight recent works in the field of Computational Optical Sensing and Imaging. Many of the works were presented at the 2021 OSA Topical Meeting on Computational Optical Sensing and Imaging, held virtually from July 19 to July 23, 2021. Articles in the feature issue cover a broad scope of computational imaging topics, such as microscopy, 3D imaging, phase retrieval, non-line-of-sight imaging, imaging through scattering media, ghost imaging, compressed sensing, and applications with new types of sensors. Deep learning approaches for computational imaging and sensing are also a focus of this feature issue.

Time perception is fluid and affected by manipulations to visual inputs. Previous literature shows that changes to low-level visual properties alter time judgments at the millisecond-level. At longer intervals, in the span of seconds and minutes, high-level cognitive effects (e.g., emotions, memories) elicited by visual inputs affect time perception, but these effects are confounded with semantic information in these inputs, and are therefore challenging to measure and control. In this work, we investigate the effect of asemantic visual properties (pure visual features devoid of emotional or semantic value) on interval time perception. Our experiments were conducted with binary and production tasks in both conventional and head-mounted displays, testing the effects of four different visual features (spatial luminance contrast, temporal frequency, field of view, and visual complexity). Our results reveal a consistent pattern: larger visual changes all shorten perceived time in intervals of up to 3min, remarkably contrary to their effect on millisecond-level perception. Our findings may help alter participants' time perception, which can have broad real-world implications., Competing Interests: Some of the authors (LH, ZB, QS) of this manuscript were employed by Adobe, Inc. or Adobe Research during this project. This does not alter our adherence to PLOS ONE policies on sharing data and materials.