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New research tackles makeup transfer with improved identity preservation · 2 sources tracked

By PulseAugur Editorial · [2 sources] ·

Two new research papers address the challenge of makeup transfer in images, aiming to apply cosmetic styles to source faces while preserving identity and fine details. The first paper, "Anchoring on Reality Makeup Transfer (ART)," introduces a two-stage framework that refines results using real reference data after an initial pseudo-target stage. It also presents MakeupFaces2K (MF2K), a new dataset of 2K-resolution makeup portraits. The second paper, "ConsistentBeauty and RealBeauty," proposes a data curation pipeline for synthetic data and a post-training framework that uses reinforcement learning to adapt models to real-world scenarios, aiming for improved identity consistency and generalization. AI

IMPACT These methods aim to improve the realism and identity preservation in AI-generated makeup applications, potentially impacting digital beauty and virtual try-on technologies.

RANK_REASON Two academic papers published on arXiv detailing new methods for image makeup transfer.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New research tackles makeup transfer with improved identity preservation · 2 sources tracked

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Two academic papers published on arXiv detailing new methods for image makeup transfer.
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COVERAGE [2]

  1. arXiv cs.CV TIER_1 English(EN) · Bo Wei, Xianhui Lin, Yi Dong, Zhongzhong Li, Zonghui Li, Zirui Wang, Jiachen Yang, Xing Liu, Hong Gu, Xiaoming Li, Wangmeng Zuo ·

    Anchoring on Reality: Breaking the Pseudo-Target Ceiling in Makeup Transfer

    arXiv:2606.31089v2 Announce Type: replace Abstract: Makeup transfer applies a reference cosmetic style to a source face while preserving its identity and geometry. However, this task is severely hindered by the lack of real paired training data. Current methods rely on either wea…

  2. arXiv cs.CV TIER_1 English(EN) · Yue Yu, Jiayu Wang, Jiajia Shi, Zhiyu Tan, Hao Li, Jingjing Chen ·

    From Synthetic to Real: Toward Identity-Consistent Makeup Transfer with Synthetic and Real Data

    arXiv:2605.07861v2 Announce Type: replace Abstract: Makeup transfer aims to apply the makeup style of a reference portrait to a source portrait while preserving identity and background. Early methods formulate this task as unsupervised image-to-image translation, relying on surro…

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