人脸被视为最重要的身份信息，在计算机领域中被广泛研究。近年来，人工智能生成内容（AI Generated Content, AIGC）在众多领域取得了显著的发展，尤其是人脸生成和人脸替换领域。利用人工智能技术快速生成逼真的换脸图像成为了火热研究的话题。本文搭建的实时人脸替换系统基于自编码器生成模型，能够便利地进行人脸数据预处理，完成后续模型训练与推理融合，实时获得逼真且相像的人脸替换图像。

在人脸数据预处理模块，本系统采用了时下最前沿的研究成果，完成了人脸提取、人脸对齐、人脸清洗和人脸分割的任务，定制源人物高质量数据集，并且设计了新颖的数据标注存储格式。在人脸生成与替换模块，本系统搭建了深度残差自编码器模型进行训练，并且对推理后的人脸数据进行融合处理工作，对于结果进行推流应用。实验结果表明，本系统所产生的人脸替换图像有着较高的相似度，生成速度可达实时要求，且便于使用理解。本文为人脸替换领域提供了新的案例，同时为直播娱乐、视频制作、伪造检测等工作提供了样本，助力AIGC发展。

Face is regarded as the most important identity information and is widely studied in the computer field. In recent years, artificial intelligence generated content ( AIGC ) has achieved remarkable development in many fields, especially in the field of face generation and face swap. Using artificial intelligence technology to quickly generate realistic face-swapped images has become a hot research topic. The real-time face swap system built in this paper is based on the Auto-Encoder generation model, which can facilitate face data preprocessing, complete subsequent model training and generating, and obtain realistic and similar face-swapped images in real time.

In the face data preprocessing module, the system adopts the SOTA research results, completes the tasks of face extraction, face alignment, face cleaning and face segmentation, customizes the high-quality data set of source characters, and designs a novel data annotation storage format. In the face generation and swap module, the system builds a deep residual Auto-Encoder model for training, and performs fusion processing on the inferred face data, and applies the results to the streaming application. The experimental results show that the face-swapped images generated by the system have high similarity, and the generation speed can meet the real-time requirement. This paper provides new cases for the field of face-swap, and provides samples for live entertainment, video production, forgery detection and other work to help the development of AIGC.