Three-Dimensional Animation Generation and Image Enhancement Technology Based on Multi-Column Convolutional Neural Network Model

This paper aims to improve the quality and fidelity of three-dimensional (3D) animation. Firstly, the application model of Multi-Column Convolutional Neural Network (MCNN) in 3D animation generation and image enhancement is proposed. Aiming at the generation of 3D animation, the MCNN algorithm suitable for this field is selected, and its working principle is explained in detail. Meanwhile, the theoretical basis of 3D animation generation is introduced, which provides a theoretical basis for subsequent experiments. Secondly, for image enhancement, the MCNN is also selected as the key technology, and its application model in image enhancement is explained. Finally, a simulation experiment is carried out to evaluate the effect of the proposed MCNN model in 3D animation generation and image enhancement. By collecting appropriate data sets and setting parameters in the corresponding experimental environment, the performance of the proposed model is evaluated. The results show that, compared with the traditional methods, the MCNN model shows better performance and effect in animation generation and image enhancement tasks. Specifically, this method can still maintain good performance under the conditions of shorter training time, faster reasoning time and lower memory occupation, and this method has advantages in computational efficiency. 3D animation generation and image enhancement technology with MCNN model can significantly improve the animation quality and image fidelity, and satisfactory experimental results have been obtained. The experimental results in this paper verify the application potential of MCNN in 3D animation generation and image enhancement, and provide new ideas and directions for further research and application.