Dynamic Capturing of Facial Expression and 3D Animation Generation based on Generative Adversarial Network

In recent years, interaction between human and computer based on the functions of computer software is distant from meeting human needs for computer use through development of computer and Artificial Intelligence (AI). People expect a more convenient and faster human-computer interface. In the mobile network environment, the accuracy of related image matching algorithms is affected by factors such as bandwidth uncertainty and channel interference, resulting in significant limitations in image feature matching. The deep learning approach of Generative Adversarial Network (GAN) is proposed for dynamic capturing of 3D animation effect generation by using facial expressions. The feature extraction approach utilized the histogram (HOG) and utilizes the GAN in classification for dynamic capture of animation effects. OpenGL and C++ are employed for 3D animation to simulate the rendering. The outcomes exhibit that the face detection approach has attained good performance in both accuracy and speed.