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Stable-Makeup: When Real-World Makeup Transfer Meets Diffusion Model
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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. Code released: https://github.com/Xiaojiu-z/Stable-Makeup
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Cited by 7 Pith papers
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EvoMakeup: High-Fidelity and Controllable Makeup Editing with MakeupQuad
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FFHQ-Makeup provides 90K paired bare/makeup images across 18K identities with five styles each, generated by a pair-free 3DMM-guided diffusion transfer method.
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Towards High-Fidelity, Identity-Preserving Real-Time Makeup Transfer: Decoupling Style Generation
A decoupled makeup-transfer pipeline with synthetic pseudo-ground-truth training achieves impressive numbers, but the evaluation is circular and code is not released.
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AvatarMakeup: Realistic Makeup Transfer for 3D Animatable Head Avatars
A coarse-to-fine pipeline transfers makeup from one reference image to an animatable 3D Gaussian avatar, using UV-map averaging for cross-view consistency and diffusion refinement for detail.
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A decoupled-attention adapter transfers image-pair edits to new photos in diffusion transformers, trained with a new 218-task visual editing dataset.
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