mlsanityData to 7 Oct 2026

Semi-parametric Makeup Transfer via Semantic-aware Correspondence

arXiv 2203.02286 ↗First posted 4 Mar 2022cs.CVcited by 4

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Abstract (arXiv)

The large discrepancy between the source non-makeup image and the reference makeup image is one of the key challenges in makeup transfer. Conventional approaches for makeup transfer either learn disentangled representation or perform pixel-wise correspondence in a parametric way between two images. We argue that non-parametric techniques have a high potential for addressing the pose, expression, and occlusion discrepancies. To this end, this paper proposes a Semi-parametric Makeup Transfer (SpMT) method, which combines the reciprocal strengths of non-parametric and parametric mechanisms. The non-parametric component is a novel Semantic-aware Correspondence (SaC) module that explicitly reconstructs content representation with makeup representation under the strong constraint of component semantics. The reconstructed representation is desired to preserve the spatial and identity information of the source image while "wearing" the makeup of the reference image. The output image is synthesized via a parametric decoder that draws on the reconstructed representation. Extensive experiments demonstrate the superiority of our method in terms of visual quality, robustness, and flexibility. Code and pre-trained model are available at urlhttps://github.com/AnonymScholar/SpMT.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 23 of its 37 references that are in the map. First posted on arXiv 4 Mar 2022.

Builds on

23 of 37 references.

References · 23

  1. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  2. 2022Generative Adversarial Networkscited by 5,603
  3. 2016Perceptual Losses for Real-Time Style Transfer and Super-Resolutioncited by 2,678
  4. 2017Non-local Neural Networkscited by 2,290
  5. 2018Glow: Generative Flow with Invertible 1x1 Convolutionscited by 1,539
  6. 2019Semantic Image Synthesis with Spatially-Adaptive Normalizationcited by 1,295
  7. 2016Least Squares Generative Adversarial Networkscited by 1,000
  8. 2019MaskGAN: Towards Diverse and Interactive Facial Image Manipulationcited by 624
  9. 2019DRIT++: Diverse Image-to-Image Translation via Disentangled Representationscited by 244
  10. 2017Visual Attribute Transfer through Deep Image Analogycited by 123
  11. 2020Cross-domain Correspondence Learning for Exemplar-based Image Translationcited by 116
  12. 2018Avatar-Net: Multi-scale Zero-shot Style Transfer by Feature Decorationcited by 115
11 more
  1. 2016Fast Patch-based Style Transfer of Arbitrary Stylecited by 92
  2. 2018Arbitrary Style Transfer with Deep Feature Reshufflecited by 75
  3. 2019PSGAN: Pose and Expression Robust Spatial-Aware GAN for Customizable Makeup Transfercited by 66
  4. 2019LADN: Local Adversarial Disentangling Network for Facial Makeup and De-Makeupcited by 63
  5. 2018Semi-parametric Image Synthesiscited by 50
  6. 2019Multimodal Style Transfer via Graph Cutscited by 32
  7. 2021SOGAN: 3D-Aware Shadow and Occlusion Robust GAN for Makeup Transfercited by 22
  8. 2021Facial Attribute Transformers for Precise and Robust Makeup Transfercited by 17
  9. 2016Makeup like a superstar: Deep Localized Makeup Transfer Networkcited by 7
  10. 2019Disentangled Makeup Transfer with Generative Adversarial Networkcited by 7
  11. 2020Local Facial Makeup Transfer via Disentangled Representationcited by 2

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Cited by year

Citing papers per year: 2024: 3; 2025: 1.

4 papers.

  1. 2024Stable-Makeup: When Real-World Makeup Transfer Meets Diffusion Modelcited by 44
  2. 2024Content-Style Decoupling for Unsupervised Makeup Transfer without Generating Pseudo Ground Truthcited by 15
  3. 2024SHMT: Self-supervised Hierarchical Makeup Transfer via Latent Diffusion Modelscited by 10
  4. 2025Towards High-Fidelity, Identity-Preserving Real-Time Makeup Transfer: Decoupling Style Generationcited by 0