A Novel AI-Based Visual Stimuli Generation Approach for Environment Concept Design.

The nature of environment concept design is a visual-based issue, where designers need to find loads of visual stimuli to create the high-quality concept. This paper aims to introduce a novel AI-based method to automatically generate images as design inspiration for environment concept design. Through interviewing eight professionals, we discovered that acquiring the design stimulus with inspired "composition" in a short time is the prioritized need of designers. This paper takes spectacular ambience for example. Through testing six classic GAN model variants trained by a self-made data set, we selected qualified four models to generate black and white thumbnails as stimuli with spectacular ambience. Moreover, we conducted a qualitative study of the outputs of the four models in a manner that invite eight designers to discuss. Finally, we summarized five key factors that influence designers' satisfaction with generated visual stimuli and discuss future directions that are worth studying.

Research on Emotion Analysis of Chinese Literati Painting Images Based on Deep Learning.

Starting from a pure-image perspective, using machine learning in emotion analysis methods to study artwork is a new cross-cutting approach in the field of literati painting and is an effective supplement to research conducted from the perspectives of aesthetics, philosophy, and history. This study constructed a literati painting emotion dataset. Five classic deep learning models were used to test the dataset and select the most suitable model, which was then improved upon for literati painting emotion analysis based on accuracy and model characteristics. The final training accuracy rate of the improved model was 54.17%. This process visualizes the salient feature areas of the picture in machine vision, analyzes the visualization results, and summarizes the connection law between the picture content of the Chinese literati painting and the emotion expressed by the painter. This study validates the possibility of combining deep learning with Chinese cultural research, provides new ideas for the combination of new technology and traditional Chinese literati painting research, and provides a better understanding of the Chinese cultural spirit and advanced factors.

A Novel Paradigm to Design Personalized Derived Images of Art Paintings Using an Intelligent Emotional Analysis Model.

With the development of artificial intelligence (AI), it is imperative to combine design methods with new technologies. From the perspective of the personalized design of derived images of art paintings, this study analyzes the new user demand generated by the current situation and background of personalized design, puts forward a new method of derivative design based on AI emotion analysis, verifies the feasibility of the new method by constructing a personalized design system of derived images of art paintings driven by facial emotion features, and explores the method of combining AI emotion recognition, emotion analysis, and personalized design. This study provides new ideas for the design of art derivatives for the future with massive personalized demand. Thinking and practicing from the perspective of the development of new technology will promote the change of design paradigms in the digital age.