A Multi-Implicit Neural Representation for Fonts

Fonts are ubiquitous across documents and come in a variety of styles. They are either represented in a native vector format or rasterized to produce fixed resolution images. In the first case, the non-standard representation prevents benefiting from latest network architectures for neural representations; while, in the latter case, the rasterized representation, when encoded via networks, results in loss of data fidelity, as font-specific discontinuities like edges and corners are difficult to represent using neural networks. Based on the observation that complex fonts can be represented by a superposition of a set of simpler occupancy functions, we introduce \textit{multi-implicits} to represent fonts as a permutation-invariant set of learned implict functions, without losing features (e.g., edges and corners). However, while multi-implicits locally preserve font features, obtaining supervision in the form of ground truth multi-channel signals is a problem in itself. Instead, we propose how to train such a representation with only local supervision, while the proposed neural architecture directly finds globally consistent multi-implicits for font families. We extensively evaluate the proposed representation for various tasks including reconstruction, interpolation, and synthesis to demonstrate clear advantages with existing alternatives. Additionally, the representation naturally enables glyph completion, wherein a single characteristic font is used to synthesize a whole font family in the target style

Understanding Hardware-Accelerated 2D Vector Graphics

With the rising support of compute kernels and low-level GPU architecture access over the past few years, friction with general-purpose GPU computing is fading. With new accessibility, new analytics methods for hardware-accelerated vector rasterization are being tried with new leverage. There are compelling reasons to optimize performance given the resolution-independent imaging model and inherent benefits. However, there is a noticeable lack of comparison between algorithms, techniques, and libraries which gauge the modern rendering capability. Analyzing the performance of vector graphics on the GPU is challenging, primarily when various technologies may compete for differing scarce computer resources. This thesis examines the contention found with modern vector graphic rendering and expands on analysis techniques used to de-obfuscate efficacy by providing an analytic benchmarking framework for hardware-accelerated renderers