A Method for Generating Full-view Facial Texture Images Based on Deep Reinforcement Learning Algorithms
Due to the complexity of facial features and the diversity of texture structures,traditional methods are often limited by integrity,texture authenticity,clarity and robustness. Therefore,a full-view facial texture image generation method based on deep reinforcement learning algorithm is proposed in this study. Firstly,the facial region of the full-view face is divided in detail,and the coordinate system is established to accurately extract the key texture structure feature points of each region. Subsequently,these feature points are input into the deep reinforcement learning model and integrated into a comprehensive set of full-view feature points through algorithmic optimization. By using Markov weight field to further process the feature points,a full-view facial texture image with rich detail and clear texture is generated by calculating the joint probability and combining the overlapping region constraints. The experimental results show that the image generated by the proposed method has higher peak signal-to-noise ratio and higher texture clarity,and is robust,which can effectively meet the needs of high-quality facial texture image generation.
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