Stable-Makeup: When Real-World Makeup Transfer Meets Diffusion Model
<|reference_start|>Stable-Makeup: When Real-World Makeup Transfer Meets Diffusion Model: Current makeup transfer methods are limited to simple makeup styles, making them difficult to apply in real-world scenarios. In this paper, we introduce Stable-Makeup, a novel diffusion-based makeup transfer method capable of robustly transferring a wide range of real-world makeup, onto user-provided faces. Stable-Makeup is based on a pre-trained diffusion model and utilizes a Detail-Preserving (D-P) makeup encoder to encode makeup details. It also employs content and structural control modules to preserve the content and structural information of the source image. With the aid of our newly added makeup cross-attention layers in U-Net, we can accurately transfer the detailed makeup to the corresponding position in the source image. After content-structure decoupling training, Stable-Makeup can maintain content and the facial structure of the source image. Moreover, our method has demonstrated strong robustness and generalizability, making it applicable to varioustasks such as cross-domain makeup transfer, makeup-guided text-to-image generation and so on. Extensive experiments have demonstrated that our approach delivers state-of-the-art (SOTA) results among existing makeup transfer methods and exhibits a highly promising with broad potential applications in various related fields.<|reference_end|>
@article{zhang2024stable-makeup:,
    title={Stable-Makeup: When Real-World Makeup Transfer Meets Diffusion Model},
    author={Yuxuan Zhang, Lifu Wei, Qing Zhang, Yiren Song, Jiaming Liu, Huaxia
  Li, Xu Tang, Yao Hu, Haibo Zhao},
    journal={arXiv preprint arXiv:2403.07764},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.07764},
    primaryClass={cs.CV}
}