Typeface design plays a vital role in graphic and communication design. Different typefaces are suitable for different contexts and can convey different emotions and messages. Typeface design still relies on skilled designers to create unique styles for specific needs. Recently, generative adversarial networks (GANs) have been applied to typeface generation, but these methods face challenges due to the high annotation requirements of typeface generation datasets, which are difficult to obtain. Furthermore, machine-generated typefaces often fail to meet designers’ specific requirements, as dataset annotations limit the diversity of the generated typefaces. In response to these limitations in current typeface generation models, we propose an alternative approach to the task. Instead of relying on dataset-provided annotations to define the typeface style vector, we introduce a transformer-based language model to learn the mapping between a typeface style description and the corresponding style vector. We evaluated the proposed model using both existing and newly created style descriptions. Results indicate that the model can generate high-quality, patent-free typefaces based on the input style descriptions provided by designers.

The current evaluation of signs relies on quantitative comprehensibility testing. Such testing yields extensive findings about signs’ effectiveness. However, a shortcoming of comprehensibility testing is that it does not provide qualitative information relevant to sign modification and does not facilitate interactions between designers and users. This article advocates the use of visual data to evaluate signs by examining the similarities between signs and drawings produced by end users based on a sign referent given to them. A new evaluation index is developed to measure the extent to which a sign conforms to users’ mental images and to determine whether it should be redesigned. It is calculated by using the learned perceptual image patch similarity. To illustrate the modified approach, a study of safety signs is presented in the article. The article provides an example of how evaluation using visual data can be conducted.

Ideation is a critical step in the engineering design process, enabling designers to develop creative and innovative concepts and prototypes. Currently, the ideation workflow requires designers to generate new designs based on product requirements, heavily relying on their personal expertise and experience. To advance human-AI collaboration design and assist designers in the idea-generation process, this paper proposes an Object Combination Generative Adversarial Network (OC-GAN) for combinational creativity. The proposed method includes an image encoder module and a cross-domain object combination generator module. The image encoder module captures and encodes image structure information into latent space, while the cross-domain object combination generator module leverages GANs to combine object images based on user preferences, producing new design images. A design case study is used to evaluate the new ideation approach and reveal not only strong cross-domain concept combination capabilities but also improvement in designers' workflow and provision of novelty to the design case. Highlights An AI approach to improve the efficiency of idea generation in the design process. A case study evaluates its support for idea generation and design creativity. The OC-GAN is used for multi-domain object image combining tasks. Exemplifies the feasibility of human-AI collaboration design for enhancing creativity.

Since traditional gaze-tracking methods rely on line-of-sight estimation, spatial attention modeling from neural activity offers an alternative perspective to gaze estimation. This paper presents a proof-of-concept study on attention-guided gaze estimation with Electroencephalography (EEG), investigating whether brain signals can be leveraged to estimate attentional focus within a controlled 3D environment. We first conducted a preliminary survey to gather public opinions, revealing a generally positive attitude towards EEG-driven gaze tracking. Building on this insight, we collected an EEG dataset in VR, where participants engaged with stimuli presented at predefined spatial locations. We introduce a deep learning model that estimates the relative saliency of candidate positions, enabling gaze estimation through optimization within the learned representation. Our results demonstrate that attentional focus was successfully mapped in a 3D coordinate space from 5 participants, and low-frequency oscillations contributed more significantly to predictive performance. The model achieved robust accuracy in distinguishing gaze locations, highlighting the potential of EEG-based gaze estimation for attention tracking in 3D environments.

With the development of the theory and technology of computer science, machine or computer painting is increasingly being explored in the creation of art. Machine-made works are referred to as artificial intelligence (AI) artworks. Early methods of AI artwork generation have been classified as non-photorealistic rendering, and, latterly, neural style transfer methods have also been investigated. As technology advances, the variety of machine-generated artworks and the methods used to create them have proliferated. However, there is no unified and comprehensive system to classify and evaluate these works. To date, no work has generalized methods of creating AI artwork including learning-based methods for painting or drawing. Moreover, the taxonomy, evaluation, and development of AI artwork methods face many challenges. This article is motivated by these considerations. We first investigate current learning-based methods for making AI artworks and classify the methods according to art styles. Furthermore, we propose a consistent evaluation system for AI artworks and conduct a user study to evaluate the proposed system on different AI artworks. This evaluation system uses six criteria: beauty, color, texture, content detail, line, and style. The user study demonstrates that the six-dimensional evaluation index is effective for different types of AI artworks.