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.

翻译：当代妆容方法主要依赖非配对学习范式，但面临监督不准确（如人脸未对齐）和复杂面部提示（包括人脸解析和关键点检测）的挑战。这些问题阻碍了人脸妆造模型在移动设备等低成本场景中的部署。为解决上述问题，我们提出一种全新的学习范式——"数据放大学习（DAL）"，同时设计名为"TinyBeauty"的轻量化妆造模型。DAL的核心思想在于利用基于扩散模型的数据放大器（DDA）对有限图像进行"放大"以用于模型训练，从而仅需少量标注即可实现精确的像素级监督。DDA的两项关键创新支撑了上述训练方法：（1）设计残差扩散模型（RDM）生成高保真细节，规避传统扩散模型中的细节消失问题；（2）提出细粒度妆造模块（FGMM），在保持人脸身份特征的同时实现精准妆造控制与组合。结合DAL，TinyBeauty仅需80K参数即可在无需复杂面部提示的情况下达到最先进性能。同时，TinyBeauty在iPhone 13上实现了高达460帧/秒的惊人推理速度。大量实验表明，DAL仅需5组图像对即可训练出极具竞争力的妆造模型。