Research on Visual Image Texture Rendering for Artistic Aided Design

In this study, the ability level of art and design students in colleges and universities was accurately calculated by adopting the Logistics model in item response theory, combined with learner-question response data, and the maximum likelihood estimation method. Further, the study constructs a personalized recommendation model based on students’ competence, which utilizes the singular value decomposition technique to calculate students’ similarity coefficients, and achieves accurate personalized learning path recommendation. Through descriptive analysis, this paper delves into the specific application and effect of this personalized recommendation algorithm in the teaching reform of art and design majors in colleges and universities. The simulation analysis method evaluates the impact of online art and design professional teaching. The results show that this algorithm performs best in scoring prediction accuracy when the regularization coefficient is set to 0.02, and its RMSE value reaches 0.95012.This finding confirms that the personalized recommendation algorithm based on the students’ ability can effectively promote the reform of art and design teaching in colleges and universities, and realize tailored teaching. In addition, through accurate learning path recommendation, the algorithm significantly improves students’ learning ability and enthusiasm and provides valuable guidance and reference for teaching reform in colleges and universities.

Section: Introductionmentioning

confidence: 93%

Research on the Teaching Reform Path of Art and Design Specialties in Colleges and Universities in the Era of Artificial Intelligence

Wen

2024

“…In order to enhance the problem of poor image texture rendering in art design, a rendering method based on the understanding algorithm of scene vision is proposed. The algorithm utilizes sampling to extract multi-target features and extracts the feature points of the extracted elements into the coordinate axes to generate an artistic image of the design, which assists in artistic design work [13]. Virtual stage integrates stage art and stage performance forms, so Zhang, Y. et al used 3D modeling technology and MEC to design stage art.…”

Section: Introductionmentioning

confidence: 99%

Research on the Innovation and Development Strategy of Stage Art Design in the Context of the New Era

2024

People’s aesthetic concepts have changed a lot in the modern context, and the relevant requirements for stage art design are also constantly improving. The purpose of this paper is to propose a strategy for the innovative development of virtual stages using augmented reality technology. Specifically, the process involves extracting stage movement frame data through the virtual environment and processing stage mechanical movement data. Combined with the stage in the actual operation of the situation, the use of linear functions to optimize the stage data and the data correction at the same time reduce the virtual stage in the movement parameters of the error. The immersive stage is constructed using augmented reality technology, and the elements of color, lighting, and music are added to the stage. Sound presentation, interactive performance, and satisfaction analysis are the three perspectives that examine the impact of stage art design. In the sound presentation, the difference between the top panels of the three heights under full stage conditions is between 5 and 7 dB, with an average value of 5.7583 dB, and the sound presentation is closer to the ideal state after full stage. Nearly 50% of the audience thought that the stage engagement was better, giving a high satisfaction score of 5. The use of augmented reality technology has enhanced the choreography’s interactivity.

“…With the increasing demand for personalization and aesthetics by modern consumers, fashion elements are widely used in ceramic design. These fashion elements may include popular colors, patterns, shapes, etc., and they not only enhance the aesthetic value of ceramic products but also increase their market competitiveness [6][7][8]. With the help of big data, designers can use big data to analyze popular trends, such as colors, patterns, etc., to predict the future design direction and analyze consumer data to understand the aesthetic preferences and purchasing behavior of different groups and other strategies to integrate fashion elements into ceramic design [9][10].…”

Section: Introductionmentioning

confidence: 99%

Discussion on the use of fashion elements in ceramic design in the context of big data

2024

In the context of the significant data era, this study explores the application of fashion elements in ceramic design. By integrating modern aesthetics and traditional craftsmanship, it realizes the innovation of ceramic design and promotes the development of ceramic industry. A neural network-based assessment model, especially BP artificial neural network, is used to quantitatively analyze and apply fashion elements in ceramic design. First, the basic theory of neural network, including MP model and BP algorithm, is introduced. The study established a network model with a 5-30-5 structure by training 800 sets of color samples. The experimental results show that the network reaches the mean square error target (MSE=1e-7) after 1516 iterations, proving the model’s effectiveness in dealing with nonlinear color semantic mapping. The analysis of the prediction accuracy of the color scheme shows that the maximum errors are 2.22, 1.93, and 1.36 in the L, A, and B components, respectively. These results are lower than the national standard color difference range, indicating that the proposed model has practical application in ceramic design. The neural network model can effectively integrate fashion elements into ceramic design, providing a new perspective and technical support for traditional ceramic design.