A Review of Multimodal Identity Reconstruction Techniques in Face Image Translation

Abstract

With the rapid development of artificial intelligence and computer vision, face image translation has been widely used in criminal analysis, medical diagnosis assistance and digital cultural heritage restoration. In these cases, the input information usually comes from sketches, attribute tags or natural language descriptions. These information usually have some problems, such as incomplete information, huge differences between patterns and uncertain semantic expression. These factors lead to identity drift in the generated results. That is to say, even if the image looks realistic, the important characteristics of identity do not match those target people. This paper proposes the application of multimodal identity reconstruction technology in face image translation system. It focuses on the analysis of application mechanism, broadcast model and multi-mode fusion method of generating countermeasure network (GANs) to keep identity. This paper summarizes the main technical methods, such as identity constraints, structural modeling and cross-modal alignment. It also determines the factors that affect identity consistency, semantic consistency and generation stability. On this basis, this paper also discusses the evaluation criteria, the generalization ability of the model and the challenges of actual deployment faced by the current research. It provides a direction for future research. This study is helpful to better understand the mechanism of multimodal identity reconstruction, provide a theoretical framework for creating a high-precision and controllable facial generation model, and provide technical support for engineering applications in related fields.