Research on Visual Design of AI Painting Based on SolidWorks Technology

Abstract

AI technology has become a new creative tool, which brings infinite possibilities for painting creation. In this paper, we first parameterize the design of the painting through SolidWorks software and construct a semantic text with the design parameters obtained. Secondly, the text is inputted into the text-image similarity model based on a graph convolutional network, and the Stanford CoreNLP tool is used to perform dependency parsing on the input text and feature extraction on the textual semantic and spatial relation maps through two GCN networks. Finally, the visual parameters and visual comfort of the AI drawing samples are examined. The results show that the maximum difference between the cumulative scores comparing the reference image and the AI output image is 0.41, and this value is less than 1, which indicates that the AI drawing achieves the visual effect of quantifying the image quality. The efficiency and quality of drawing output can be improved through this study, which has both theoretical and practical significance.

Keywords

• solidworks
• parametric design
• semantic text
• text-image similarity
• ai drawing
• 97m50