Research on deep learning-based algorithms for computational graphics generation and optimization

With the continuous development of computer graphics technology, how to generate and optimize graphics efficiently and realistically has become an important research topic in the field of graphics and artificial intelligence. In recent years, deep learning, with its powerful feature extraction and expression ability, provides a new solution idea for the automatic generation and optimization of computational graphics. This paper focuses on the systematic study of computational graphics generation and optimization algorithms based on deep learning, firstly, discusses the key models and applications of deep learning in graphics generation, analyzes its advantages in image detail generation and complex structure modeling; secondly, it studies in-depth deep learning-driven graphics optimization algorithms, proposes a variety of optimization strategies and compares them with traditional algorithms, highlighting its significant improvement in efficiency and effectiveness; Finally, combined with the trend of integration of graph generation and optimization, an integrated deep learning framework is proposed to realize the integrated processing of generation and optimization, and improve the overall graph quality and processing speed. Through theoretical analysis and experimental verification, this paper demonstrates the important role of deep learning in promoting the development of computational graphics intelligence and automation, and provides a feasible path and reference direction for subsequent research.