Contemporary makeup approaches primarily hinge on unpaired learning paradigms, yet they grapple with the challenges of inaccurate supervision (e.g., face misalignment) and sophisticated facial prompts (including face parsing, and landmark detection). These challenges prohibit low-cost deployment of facial makeup models, especially on mobile devices. To solve above problems, we propose a brand-new learning paradigm, termed "Data Amplify Learning (DAL)," alongside a compact makeup model named "TinyBeauty." The core idea of DAL lies in employing a Diffusion-based Data Amplifier (DDA) to "amplify" limited images for the model training, thereby enabling accurate pixel-to-pixel supervision with merely a handful of annotations. Two pivotal innovations in DDA facilitate the above training approach: (1) A Residual Diffusion Model (RDM) is designed to generate high-fidelity detail and circumvent the detail vanishing problem in the vanilla diffusion models; (2) A Fine-Grained Makeup Module (FGMM) is proposed to achieve precise makeup control and combination while retaining face identity. Coupled with DAL, TinyBeauty necessitates merely 80K parameters to achieve a state-of-the-art performance without intricate face prompts. Meanwhile, TinyBeauty achieves a remarkable inference speed of up to 460 fps on the iPhone 13. Extensive experiments show that DAL can produce highly competitive makeup models using only 5 image pairs.

通过使用Diffusion-based Data Amplifier (DDA)作为Data Amplify Learning (DAL)的核心思想，结合一个名为TinyBeauty的紧凑化妆模型，我们提出了一种全新的学习范式来解决当前化妆模型的监督和面部提示的挑战，以实现低成本部署化妆模型在移动设备上的应用。DAL能够以仅使用少数标注图像即可实现像素级准确监督，而TinyBeauty只需80K个参数即可实现最先进的性能，并在iPhone 13上获得了惊人的推理速度，高达460帧/秒。我们广泛的实验证明，DAL可以仅使用5个图像对产生具有极高竞争力的化妆模型。

走向微型高质量的面部化妆