High-Quality Graphic Rendering and Synthesis of Design Graphics Using Generating Adversarial Networks

High-quality graphic rendering can accurately show the appearance, material, and lighting effects of design objects, thus improving the accuracy of design decisions. Image synthesis technology can skillfully integrate multiple design elements, create a harmonious and unified scene, and then strengthen the overall sense and visual impact of design. In this article, we innovatively propose an algorithm for image generation by using the Generating adversarial network (GAN). This algorithm can deeply learn the internal distribution law of real images and then produce high-quality rendering results. By constructing a reinforcement learning (RL) model, we skillfully transform the generation process of GAN into a series of decision-making problems and constantly seek the optimal generation strategy with the help of real-time interaction between agents and the environment. In the experiment, we made a comprehensive comparison with the traditional method. The findings indicate that the novel approach has attained a notable advancement in rendering velocity, accompanied by an enhancement in rendering quality exceeding 10%. This remarkable achievement is mainly attributed to the perfect combination of real-time rendering technology and dynamic adjustment strategy. This research not only injects new vitality into the field of graphic rendering but also provides solid technical support for improving efficiency and quality assurance in design practice. © 2024 U-turn Press LLC.