Learning Perceptual Manifold of Fonts
Along the rapid development of deep learning techniques in generative models, it is becoming an urgent issue to combine machine intelligence with human intelligence to solve the practical applications. Motivated by this methodology, this work aims to adjust the machine generated character fonts with the effort of human workers in the perception study. Although numerous fonts are available online for public usage, it is difficult and challenging to generate and explore a font to meet the preferences for common users. To solve the specific issue, we propose the perceptual manifold of fonts to visualize the perceptual adjustment in the latent space of a generative model of fonts. In our framework, we adopt the variational autoencoder network for the font generation. Then, we conduct a perceptual study on the generated fonts from the multi-dimensional latent space of the generative model. After we obtained the distribution data of specific preferences, we utilize manifold learning approach to visualize the font distribution. In contrast to the conventional user interface in our user study, the proposed font-exploring user interface is efficient and helpful in the designated user preference.
Code (0)
등록된 구현이 없습니다.
Tasks
Font GenerationSimilar Papers 제목 키워드 기반
Exploratory Font Selection Using Crowdsourced Attributes
This paper presents interfaces for exploring large collections of fonts for design tasks. Existing interfaces typically list fonts in a long, alphabetically-sorted menu that can be challenging and frustrating to explore.…
AttributeDescriptiveJoint Implicit Neural Representation for High-fidelity and Compact Vector Fonts
Existing vector font generation approaches either struggle to preserve high-frequency corner details of the glyph or produce vector shapes that have redundant segments, which hinders their applications in practical s…
Font GenerationA Deep Factorization of Style and Structure in Fonts
We propose a deep factorization model for typographic analysis that disentangles content from style. Specifically, a variational inference procedure factors each training glyph into the combination of a character-specifi…
Variational InferenceFontGuard: A Robust Font Watermarking Approach Leveraging Deep Font Knowledge
The proliferation of AI-generated content brings significant concerns on the forensic and security issues such as source tracing, copyright protection, etc, highlighting the need for effective watermarking technologies. …
Contrastive LearningWhich Models have Perceptually-Aligned Gradients? An Explanation via Off-Manifold Robustness
One of the remarkable properties of robust computer vision models is that their input-gradients are often aligned with human perception, referred to in the literature as perceptually-aligned gradients (PAGs). Despite onl…
DenoisingImage Generation