Numerous techniques for 3D face reconstruction from a single image exist, making use of large facial databases. However, they commonly encounter quality issues due to the absence of information from alternate perspectives. For example, 3D reconstruction with a single front view input data has limited realism, particularly for profile views. We have observed that multiple-view 3D face reconstruction yields higher-quality models compared to single-view reconstruction. Based on this observation, we propose a novel pipeline that combines several deep-learning methods to enhance the quality of reconstruction from a single frontal view. Our method requires only a single image (front view) as input, yet it generates multiple realistic facial viewpoints using various deep-learning networks. These viewpoints are utilized to create a 3D facial model, significantly enhancing the 3D face quality. Traditional image-space editing has limitations in manipulating content and styles while preserving high quality. However, editing in the latent space, which is the space after encoding or before decoding in a neural network, offers greater capabilities for manipulating a given photo. Motivated by the ability of neural networks to generate 2D images from an extensive database and recognizing that multi-view 3D face reconstruction outperforms single-view approaches, we propose a new pipeline. This pipeline involves latent space manipulation by first finding a latent vector corresponding to a given image using the Generative Adversarial Network (GAN) inversion method. We then search for nearby latent vectors to synthesize multiple pose images from the provided input image, aiming to enhance 3D face reconstruction. The generated images are then fed into Diffusion models, another image synthesis network, to generate their respective profile views. The Diffusion model is known for producing more realistic large-angle variations of a given object than GAN models do. Subsequently, all these images (multi-view images) are fed into an Autoencoder, a neural network designed for 3D face model predictions, to derive the 3D structure of the face. Finally, the texture of the 3D face model is combined to enhance its realism, and certain areas of the 3D shape are refined to correct any unrealistic aspects. Our experimental results validate the effectiveness and efficiency of our method in reconstructing highly accurate 3D models of human faces from a single input (front view input) image. The reconstructed models retain high visual fidelity to the original image, even without the need for a 3D database.

Much about visual spatial attention has been learned from studying how observers respond to two-dimensional stimuli. Less is known about how attention varies along the depth axis. Most of the work on the effect of depth on spatial attention manipulated binocular disparity defined depth, and it is less clear how monocular depth cues affect spatial attention. This thesis investigates the effect of target distance on peripheral detection in a virtual three-dimensional environment that simulated distance using pictorial and motion cues. Participants followed a lead car at a constant distance actively or passively, while travelling along a straight trajectory. The horizontal distribution of attention was measured using a peripheral target detection task. Both car-following and peripheral detection were tested alone under focussed attention, and simultaneously under divided attention. Chapter 2 evaluated the effect of target distance and eccentricity on peripheral detection. Experiment 1 found an overall near advantage that increased at larger eccentricities. Experiment 2 examined the effect of anticipation on target detection and found that equating anticipation across distances drastically reduced the effect of distance in reaction time, but did not affect accuracy. Experiments 3 and 4 examined the relative contributions of pictorial cues on the effect of target distance and found that the background texture that surrounded the targets could explain the main effect of distance but could not fully account for the interaction between distance and eccentricity. Chapter 3 extended the findings of Chapter 2 and found that the effect of distance on peripheral detection in our conditions was non-monotonic and did not depend on fixation distance. Across chapters, dividing attention between the central car-following and peripheral target detection tasks consistently resulted in costs for car-following, but not for peripheral detection. This work has implications for understanding spatial attention and design of advanced driver assistance systems. / Dissertation / Doctor of Science (PhD) / Our visual world is complex and dynamic, and spatial attention enables us to focus on certain relevant locations of our world. However, much of what we know about spatial attention has been studied in the context of a two-dimensional plane, and less is known about how it varies in the third dimension: depth. This thesis aims to better understand how spatial attention is affected by depth in a virtual three-dimensional environment, particularly in a driving context. Generally, driving was simulated using a car-following task, spatial attention was measured in a task that required detecting targets appearing at different depths indicated by cues perceivable with one eye. The results of this work add to the literature that suggests that spatial attention is affected by depth and contributes to our understanding of how attention may be allocated in space. Additionally, this thesis may have implications for the design of in-car warning systems.

Previous research suggests that cellular phone conversations or similar auditory/conversational tasks lead to degradations in visual processing. Three contemporary theories make different claims about the nature of the degradation that occurs when we talk on a cellular phone. We are either: (a) disproportionately more likely to miss objects located in the most peripheral areas of the visual environment due to a reduction in the size of the attentional window or functional field of view (Atchley & Dressel, 2004); (b) more likely to miss objects from all areas of the visual environment (even at the center of fixation) because attention is withdrawn from the roadway, leading to inattention blindness or general interference (Strayer & Drews, 2006; Crundall, Underwood, & Chapman, 1999; 2002), or (c) more likely to miss objects that are located on the side of the visual environment contralateral to the cellular phone message due to crossmodal links in spatial attention (Driver & Spence, 2004). These three theories were compared by asking participants to complete central and peripheral visual tasks (i.e., a measure of the functional field of view) in isolation and in combination with an auditory task. During the combined visual/auditory task, peripheral visual targets could appear on the same side as auditory targets or on the opposite side. When the congruency between auditory and visual target locations was not considered (as is typical in previous research), the results were consistent with the general interference/inattention blindness theory, but not the reduced functional field of view theory. Yet, when congruency effects were considered, the results support the theory that crossmodal links affect the spatial allocation of attention: Participants were better at detecting and localizing visual peripheral targets and at generating words for the auditory task if attention was directed to the same location in both modalities.

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