Dual-domain adversarial training for realistic 4D facial makeup detection and generation

Abstract

A dual-domain adversarial training framework is presented for high-fidelity 4D facial makeup detection and generation under complex and variable conditions. This approach employs paired generator-discriminator architectures, each responsible for either makeup or non-makeup facial sequences, integrated via explicit cross-domain feature alignment modules. Using composite loss strategies such as knowledge and adversarial loss, the system enforces consistent and realistic generation on both fronts. Temporal regularization and temporal regularization are among these methods. The sequence learning layer and the cascaded 3D convolutional layers enhance spatiotemporal modeling to achieve robust feature abstraction in facial dynamics. At the same time, by improving dynamic data, the training distribution and system adaptability have been enhanced. The extensive validation of the large-scale 4D annotated facial dataset shows significant improvements in recognition accuracy, generation stability, and structural preservation compared to traditional and deep learning standards. Quantitative analysis shows consistent resilience to severe illumination changes, occlusion, and pose variation. Ablation experiments indicate that each module is necessary, and efficiency evaluations show that the method is scalable. The results indicate that developing domain-aware alignment and hybrid loss integration techniques is beneficial for effective facial analysis in both controlled and challenging environments. This study promotes the practical application of intelligent human-computer interaction, providing various technical solutions for realistic 4D facial recognition and modeling.