Optimal design of animation character modeling and motion capture based on deep neural network

In terms of animated character modeling, this paper proposes a generative model architecture that combines convolutional neural networks (CNN) and generative adversarial networks (GAN), and incorporates attention mechanisms to enhance the model's attention to key features. By combining conditional GAN and perceptual loss, the model can generate high-quality and diverse animated character shapes. Experimental results show that this method is significantly better than traditional GAN and variational autoencoder (VAE) in Fréchet Inception Distance (FID) and user ratings. In terms of motion capture, a preprocessing method based on denoising autoencoder and temporal convolutional network (TCN) was designed to remove noise and jitter from motion data, and a long short-term memory network (LSTM) was used to construct an action generation model, achieving smooth transitions and natural expression of actions. Through model compression and GPU acceleration technology, the system performs excellently in real-time performance, with a single frame processing delay reduced to 5.8ms, meeting the requirements of real-time interaction. The experiment verified the advantages of the proposed method in high dynamic motion capture and complex style modeling generation, providing a new technological path for the intelligent development of the animation industry.