Graphic Design of 3D Animation Scenes Based on Deep Learning and Information Security Technology
DOI:
https://doi.org/10.13052/jicts2245-800X.1135Keywords:
Generative adversarial network models, 3D animated scene images, graphic design, point cloud visualizationAbstract
This paper aims to use the improved Generative Adversarial Network (GAN) model for Three Dimensional (3D) animation graphic design, improve the efficiency of 3D animation graphic design, and promote the accuracy of model recognition. It acquires 3D animated scene color images from different perspectives. This paper performs 3D visualization through point clouds, outputs high-quality point cloud results, and uses Convolutional Neural Network (CNN), Earth-Mover (EM) distance, and Least Squares Method (LSM) to improve the GAN model. Finally, the effectiveness of the improved GAN in the graphic design of 3D animation scenes and the effects of different improved models in generating 3D animation scene images are analyzed. The results show that the computational loss amplitude of the improved GAN model using Label Smoothing processing deep convolutional neural network is between [2, 3]. The generator loss variation is smaller, and the image quality of the generated 3D animation scene is gradually improved. The training process of the LSM-improved model is more stable, and the loss value is lower than that of the EM distance improved model. The loss value of the generator is [0.3,0.5], and the loss value of the discriminator is [0.1,0.2]. The Inception score of the LSM-improved model is 0.0297 higher than that of the CNN-improved model and the EM distance improved model and 0.0198 higher than that of the GAN model.
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