IPMT: An Identity-Preserving Makeup Transfer Model

Makeup transfer serves as an important application in the field of image processing.Makeup transfer aims to transfer a reference makeup style to a source image, thereby synthesizing a high-fidelity facial image. However, when processing heavy or artistic makeup, existing generative methods frequently corrupt the identity-specific geometric features of the face. To address this issue, we propose an Identity-Preserving Makeup Transfer (IPMT) model, which is implemented via an end-to-end self-supervised framework. Specifically, we construct a multi-stream encoding pipeline and integrate a Dynamic Fusion Block to perform pixel-level adaptive fusion, achieving rigorous alignment between makeup semantics and target spatial geometry. Furthermore, to effectively prevent structural deformation without relying on pseudo-paired data, a one-step denoising approximation strategy is introduced during the training phase. By incorporating a parsing consistency loss and a 3D vertex loss during the early timesteps of generation, our method imposes explicit constraints on both the 2D planar layout and the 3D topological structure of the face. Extensive experiments demonstrate that the proposed IPMT significantly mitigates the identity inconsistency problem while ensuring high-fidelity makeup transfer.