Approaching Under-Explored Image-Space Problems with Optimization

Abstract

This doctoral dissertation delves into three distinct yet interconnected problems in the realm of interactive image-space computing in computer graphics, each of which has not been tackled by existing literature.The first problem centers on the prediction of visual error metrics in real-time applications, specifically in the context of content-adaptive shading and shading reuse. Utilizing convolutional neural networks, this research aims to estimate visual errors without requiring reference or rendered images. The models developed can account for 70%–90% of the variance and achieve computation times that are an order of magnitude faster than existing methods. This enables a balance between resource-saving and visual quality, particularly in deferred shading pipelines, and can achieve up to twice the performance compared to state-of-the-art methods depending on the portion of unseen image regions. The second problem focuses on the burgeoning field of light-field cameras and the challenges associated with depth prediction. This research argues for the refinement of cost volumes rather than depth maps to increase the accuracy of depth predictions. A set of cost-volume refinement algorithms is proposed, which dynamically operate at runtime to find optimal solutions, thereby enhancing the accuracy and reliability of depth estimation in light fields.The third problem tackles the labor-intensive nature of hand-drawn animation, specifically in the detailing of character eyes. An unsupervised network is introduced that blends inpainting and image-to-image translation techniques. This network employs a novel style-aware clustering method and a dual-discriminator optimization strategy with a triple-reconstruction loss. The result is an improvement in the level of detail and artistic consistency in hand-drawn animation, preferred over existing work 95.16% of the time according to a user study.Optimization techniques are the common thread that ties these problems together. While dynamic optimization at runtime is employed for cost volume refinement, deep-learning methods are used offline to train global solutions for the other two problems. This research not only fills gaps in the existing literature but also paves the way for future explorations in the field of computer graphics and optimization, offering new avenues for both academic research and practical applications.