No Arabic abstract
Emergent in the field of head mounted display design is a desire to leverage the limitations of the human visual system to reduce the computation, communication, and display workload in power and form-factor constrained systems. Fundamental to this reduced workload is the ability to match display resolution to the acuity of the human visual system, along with a resulting need to follow the gaze of the eye as it moves, a process referred to as foveation. A display that moves its content along with the eye may be called a Foveated Display, though this term is also commonly used to describe displays with non-uniform resolution that attempt to mimic human visual acuity. We therefore recommend a definition for the term Foveated Display that accepts both of these interpretations. Furthermore, we include a simplified model for human visual Acuity Distribution Functions (ADFs) at various levels of visual acuity, across wide fields of view and propose comparison of this ADF with the Resolution Distribution Function of a foveated display for evaluation of its resolution at a particular gaze direction. We also provide a taxonomy to allow the field to meaningfully compare and contrast various aspects of foveated displays in a display and optical technology-agnostic manner.
Foveated image reconstruction recovers full image from a sparse set of samples distributed according to the human visual systems retinal sensitivity that rapidly drops with eccentricity. Recently, the use of Generative Adversarial Networks was shown to be a promising solution for such a task as they can successfully hallucinate missing image information. Like for other supervised learning approaches, also for this one, the definition of the loss function and training strategy heavily influences the output quality. In this work, we pose the question of how to efficiently guide the training of foveated reconstruction techniques such that they are fully aware of the human visual systems capabilities and limitations, and therefore, reconstruct visually important image features. Due to the nature of GAN-based solutions, we concentrate on the humans sensitivity to hallucination for different input sample densities. We present new psychophysical experiments, a dataset, and a procedure for training foveated image reconstruction. The strategy provides flexibility to the generator network by penalizing only perceptually important deviations in the output. As a result, the method aims to preserve perceived image statistics rather than natural image statistics. We evaluate our strategy and compare it to alternative solutions using a newly trained objective metric and user experiments.
Traditional high-quality 3D graphics requires large volumes of fine-detailed scene data for rendering. This demand compromises computational efficiency and local storage resources. Specifically, it becomes more concerning for future wearable and portable virtual and augmented reality (VR/AR) displays. Recent approaches to combat this problem include remote rendering/streaming and neural representations of 3D assets. These approaches have redefined the traditional local storage-rendering pipeline by distributed computing or compression of large data. However, these methods typically suffer from high latency or low quality for practical visualization of large immersive virtual scenes, notably with extra high resolution and refresh rate requirements for VR applications such as gaming and design. Tailored for the future portable, low-storage, and energy-efficient VR platforms, we present the first gaze-contingent 3D neural representation and view synthesis method. We incorporate the human psychophysics of visual- and stereo-acuity into an egocentric neural representation of 3D scenery. Furthermore, we jointly optimize the latency/performance and visual quality, while mutually bridging human perception and neural scene synthesis, to achieve perceptually high-quality immersive interaction. Both objective analysis and subjective study demonstrate the effectiveness of our approach in significantly reducing local storage volume and synthesis latency (up to 99% reduction in both data size and computational time), while simultaneously presenting high-fidelity rendering, with perceptual quality identical to that of fully locally stored and rendered high-quality imagery.
Generative image modeling techniques such as GAN demonstrate highly convincing image generation result. However, user interaction is often necessary to obtain the desired results. Existing attempts add interactivity but require either tailored architectures or extra data. We present a human-in-the-optimization method that allows users to directly explore and search the latent vector space of generative image modeling. Our system provides multiple candidates by sampling the latent vector space, and the user selects the best blending weights within the subspace using multiple sliders. In addition, the user can express their intention through image editing tools. The system samples latent vectors based on inputs and presents new candidates to the user iteratively. An advantage of our formulation is that one can apply our method to arbitrary pre-trained model without developing specialized architecture or data. We demonstrate our method with various generative image modeling applications, and show superior performance in a comparative user study with prior art iGAN.
Computer-generated holographic (CGH) displays show great potential and are emerging as the next-generation displays for augmented and virtual reality, and automotive heads-up displays. One of the critical problems harming the wide adoption of such displays is the presence of speckle noise inherent to holography, that compromises its quality by introducing perceptible artifacts. Although speckle noise suppression has been an active research area, the previous works have not considered the perceptual characteristics of the Human Visual System (HVS), which receives the final displayed imagery. However, it is well studied that the sensitivity of the HVS is not uniform across the visual field, which has led to gaze-contingent rendering schemes for maximizing the perceptual quality in various computer-generated imagery. Inspired by this, we present the first method that reduces the perceived speckle noise by integrating foveal and peripheral vision characteristics of the HVS, along with the retinal point spread function, into the phase hologram computation. Specifically, we introduce the anatomical and statistical retinal receptor distribution into our computational hologram optimization, which places a higher priority on reducing the perceived foveal speckle noise while being adaptable to any individuals optical aberration on the retina. Our method demonstrates superior perceptual quality on our emulated holographic display. Our evaluations with objective measurements and subjective studies demonstrate a significant reduction of the human perceived noise.
Interaction in virtual reality (VR) environments is essential to achieve a pleasant and immersive experience. Most of the currently existing VR applications, lack of robust object grasping and manipulation, which are the cornerstone of interactive systems. Therefore, we propose a realistic, flexible and robust grasping system that enables rich and real-time interactions in virtual environments. It is visually realistic because it is completely user-controlled, flexible because it can be used for different hand configurations, and robust because it allows the manipulation of objects regardless their geometry, i.e. hand is automatically fitted to the object shape. In order to validate our proposal, an exhaustive qualitative and quantitative performance analysis has been carried out. On the one hand, qualitative evaluation was used in the assessment of the abstract aspects such as: hand movement realism, interaction realism and motor control. On the other hand, for the quantitative evaluation a novel error metric has been proposed to visually analyze the performed grips. This metric is based on the computation of the distance from the finger phalanges to the nearest contact point on the object surface. These contact points can be used with different application purposes, mainly in the field of robotics. As a conclusion, system evaluation reports a similar performance between users with previous experience in virtual reality applications and inexperienced users, referring to a steep learning curve.