DreamMakeup: Face Makeup Customization using Latent Diffusion Models

Geon Yeong Park, Inhwa Han, Serin Yang, Seongmin Jeong, Heechan Jeon, Myeongjin Goh, Sung Won Yi, Jin Nam, Jong Chul Ye

Keywords: Diffusion model, Makeup customization, Image editing

TL;DR: DreamMakup is a novel face makeup customization framework based on diffusion model without any fine-tuning

Abstract:

The exponential growth of the global makeup market has paralleled advancements in virtual makeup simulation technology. Despite the progress led by GANs, their application still encounters significant challenges, including training instability and limited customization capabilities. Addressing these challenges, this paper introduces DreamMakup: a novel Diffusion model based Makeup Customization, leveraging the inherent advantages of diffusion models for superior controllability and precise real-image editing. DreamMakeup employs early-stopped DDIM inversion to preserve the facial structure and identity while enabling extensive customization through various conditioning inputs such as reference images, specific RGB colors, and textual descriptions. Our model demonstrates notable improvements over existing GAN-based frameworks, improved customization, color-matching capabilities, and compatibility with textual descriptions or LLMs with affordable computational costs. Project page is available at
https://dreammakeup.github.io/

Primary Area: generative models

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Submission Number: 9085