Image style transfer is a popular research topic in the fields of computer graphics and multimedia, especially artistic stylization. The image style learning algorithm mainly studies the use of computer graphics and machine learning methods for automatic artistic rendering and intelligent processing of real sample data. The current mainstream methods mainly focus on learning static samples of artistic images. However, the information contained in static sample data is isolated and discontinuous; it is difficult to ensure the overall consistency of image style transfer. This article aims to recognize the real gesture movements of painters during the process of image style transfer, and propose a theoretical model and design method for image style transfer based on sequence task learning theory. Mainly completed the following research work: (1) Complete the design path and verification experiment of human-computer interaction gesture recognition in the air, use gesture recognition model, color space, color distribution model, color point probability, and maximum inter class variance method to complete the binary processing of images, and use Babbitt distance and learners to learn the intrinsic patterns of actions, ensuring that the gesture recognition design method in the air can be implemented; (2) Analyze and understand the real gesture process of painters, and design a dynamic decision-making model for image style transfer based on parameter exploration. Implement an image style transfer assistance system based on human-computer interaction gesture recognition.

Yanyu Liu, Zhihong Zhang, Bo Li. A method for image style transfer based on human-computer interaction gesture recognition. Academic Journal of Computing & Information Science (2024), Vol. 7, Issue 11: 50-58. https://doi.org/10.25236/AJCIS.2024.071107.

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