Research on Automatic Generation of Animation Characters and Motion Capture Technology Based on Deep Learning

This paper studies the automatic generation system of animation characters and motion capture technology based on deep learning. The system can automatically create highly expressive animation characters using deep learning algorithms, and realize accurate motion simulation of characters through motion capture technology. First, by designing a deep neural network model, combined with CNN and GAN, the generation of character images is optimized. The model can automatically generate the appearance and details of the character that meet the set requirements according to the input parameters. Secondly, the system adopts a motion capture method based on deep learning. Through the preprocessing and feature extraction of motion data, the RNN is used to model the motion trajectory, and the high-precision capture and reconstruction of the character action is achieved. The experimental results show that the accuracy of the generated character appearance and motion capture reaches 95% and 92% respectively. The system can complete the generation and motion capture of characters in a short time, and has high computational efficiency, which can meet the real-time needs in animation production and game development.

I. Introduction

With the vigorous development of the digital entertainment industry, animation production and game development have become one of the most popular research fields. Especially in the field of character generation and motion capture technology, the application of deep learning algorithms has brought unprecedented innovation to the animation production process. Traditional animation character design and motion capture methods often rely on manual drawing and manual entry of motion data, which is inefficient and has limited accuracy. In recent years, with the rapid development of deep learning technology, researchers have made significant progress in the field of animation character generation and motion capture through the self-learning and automation capabilities of deep learning models.