Deep Neural Network and Human-Computer Interaction Technology in the Field of Art Design

Traditional art design is usually based on the designer’s intuitive creativity. Limited by individual experience, knowledge and imagination, it is difficult to create more abundant and higher quality works, and the workload is huge, which limits the production efficiency of artworks. Through deep neural networks and human-computer interaction technology, the quality of art design can be improved; the workload and cost of designers can be reduced, and more artistic inspiration and tools can be provided to designers. The main contribution of this paper is to propose the use of a Cycle Generative Adversarial Network (Cycle GAN) to realize the automatic conversion of text to image and provide an immersive art experience through human-computer interaction technology such as virtual reality. In addition, the target audience of this paper is art designers and researchers of human-computer interaction technology, aiming to help them break through the traditional creation mode and lead art design to diversification and avant-garde. The content loss rate of character image conversion in Cycle GAN was reduced by 74.5% compared with that of human image conversion. The average peak signal-to-noise ratio of figure images generated by Cycle GAN was 57.9% higher than that of figure images generated by the artificial method. The character images generated by Cycle GAN reduce content loss and are more realistic. Deep neural networks and human-computer interaction technology can promote the development and progress of art design, break the traditional creative mode and bondage, and lead art to be more diversified and avant-garde.

@article{Guo2024,
  title     = {Deep Neural Network and Human-Computer Interaction Technology in the Field of Art Design},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {9},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Lan Guo and Lisha Luo and Weiquan Fan},
  doi       = {10.14569/IJACSA.2024.0150907},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150907}
}