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Fonts are ubiquitous across documents and come in a variety of styles. Theyare either represented in a native vector format or rasterized to produce fixedresolution images. In the first case, the non-standard representation preventsbenefiting 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 edgesand corners are difficult to represent using neural networks. Based on theobservation that complex fonts can be represented by a superposition of a setof simpler occupancy functions, we introduce \textit{multi-implicits} torepresent fonts as a permutation-invariant set of learned implict functions,without losing features (e.g., edges and corners). However, whilemulti-implicits locally preserve font features, obtaining supervision in theform of ground truth multi-channel signals is a problem in itself. Instead, wepropose how to train such a representation with only local supervision, whilethe proposed neural architecture directly finds globally consistentmulti-implicits for font families. We extensively evaluate the proposedrepresentation for various tasks including reconstruction, interpolation, andsynthesis to demonstrate clear advantages with existing alternatives.Additionally, the representation naturally enables glyph completion, wherein asingle characteristic font is used to synthesize a whole font family in thetarget style.

A Multi-Implicit Neural Representation for Fonts