ECGNet-Potential Diffusion Model for Optimized and Robust Animated Character Generation

There have been a large number of research results on neural networks in the field of animation, but most of the existing research focuses on specific techniques or single modal driving sources. In this paper, to achieve fast generation and optimization of animated characters, based on the potential diffusion model of convolutional neural network, combined with the potential spatial vectors of the Markov chain (including pixel-level image compression and potential denoising diffusion), we propose the application of ECGNet-potential diffusion model in animated character generation, including the edge-segmentation cross-guidance network and the overall modeling framework of animated characters (including facial animation generation, 3D face reconstruction, and attribute guidance module). Experiments with multiple datasets reveal that the latent space vector model outperforms the individual models, and its accuracy is improved by about 10%. The ECGNet-potential diffusion model proposed in this paper improves by 2.3% and 2.14% on the dataset compared to the ECGNet model, which fully verifies that the ECGNet-potential diffusion model is very effective in improving the animated character representation and feature fusion layers. The ECGNet-potential diffusion model improves 3.93% and 3.74% on the dataset compared to the suboptimal baseline model, which verifies that the ECGNet-potential diffusion model improves 3.93% and 3.74% on the dataset compared to the suboptimal baseline model. 3.74%, which validates the superiority of the ECGNet-potential diffusion model. In addition, the ECGNet-potential diffusion model achieves the best results on the dataset at the same time, which also proves the robustness of the ECGNet-potential diffusion model. The ECGNet-potential diffusion model is effective in improving the animated character representation layer and the feature fusion layer, and it shows the superiority and robustness in the prediction accuracy, the convergence speed of the training and the handling of different intensity of noises, which verifies that the proposed method is effective in animated character generation and optimization.