mlsanityData to 7 Oct 2026

Gorgeous: Create Your Desired Character Facial Makeup from Any Ideas

arXiv 2404.13944 ↗First posted 22 Apr 2024cs.CV cs.MMcited by 1

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Abstract (arXiv)

Contemporary makeup transfer methods primarily focus on replicating makeup from one face to another, considerably limiting their use in creating diverse and creative character makeup essential for visual storytelling. Such methods typically fail to address the need for uniqueness and contextual relevance, specifically aligning with character and story settings as they depend heavily on existing facial makeup in reference images. This approach also presents a significant challenge when attempting to source a perfectly matched facial makeup style, further complicating the creation of makeup designs inspired by various story elements, such as theme, background, and props that do not necessarily feature faces. To address these limitations, we introduce Gorgeous, a novel diffusion-based makeup application method that goes beyond simple transfer by innovatively crafting unique and thematic facial makeup. Unlike traditional methods, Gorgeous does not require the presence of a face in the reference images. Instead, it draws artistic inspiration from a minimal set of three to five images, which can be of any type, and transforms these elements into practical makeup applications directly on the face. Our comprehensive experiments demonstrate that Gorgeous can effectively generate distinctive character facial makeup inspired by the chosen thematic reference images. This approach opens up new possibilities for integrating broader story elements into character makeup, thereby enhancing the narrative depth and visual impact in storytelling.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 65 of its 103 references that are in the map. First posted on arXiv 22 Apr 2024.

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65 of 103 references.

References · 65

  1. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  2. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  3. 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,539
  4. 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understandingcited by 6,322
  5. 2022Classifier-Free Diffusion Guidancecited by 5,717
  6. 2023Adding Conditional Control to Text-to-Image Diffusion Modelscited by 5,466
  7. 2023SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesiscited by 4,290
  8. 2021GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Modelscited by 3,595
  9. 2022DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generationcited by 3,286
  10. 2022InstructPix2Pix: Learning to Follow Image Editing Instructionscited by 2,591
  11. 2022An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversioncited by 2,231
  12. 2021SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equationscited by 1,903
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  1. 2022RePaint: Inpainting using Denoising Diffusion Probabilistic Modelscited by 1,419
  2. 2022Imagic: Text-Based Real Image Editing with Diffusion Modelscited by 1,172
  3. 2021Blended Diffusion for Text-driven Editing of Natural Imagescited by 924
  4. 2022eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoiserscited by 921
  5. 2022Plug-and-Play Diffusion Features for Text-Driven Image-to-Image Translationcited by 897
  6. 2021Resolution-robust Large Mask Inpainting with Fourier Convolutionscited by 730
  7. 2021CogView: Mastering Text-to-Image Generation via Transformerscited by 691
  8. 2018BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentationcited by 645
  9. 2021DiffusionCLIP: Text-Guided Diffusion Models for Robust Image Manipulationcited by 640
  10. 2023Zero-shot Image-to-Image Translationcited by 503
  11. 2023DreamSim: Learning New Dimensions of Human Visual Similarity using Synthetic Datacited by 322
  12. 2022CogView2: Faster and Better Text-to-Image Generation via Hierarchical Transformerscited by 305
  13. 2022Inversion-Based Style Transfer with Diffusion Modelscited by 290
  14. 2022SmartBrush: Text and Shape Guided Object Inpainting with Diffusion Modelcited by 276
  15. 2021Large Scale Image Completion via Co-Modulated Generative Adversarial Networkscited by 219
  16. 2022MAT: Mask-Aware Transformer for Large Hole Image Inpaintingcited by 216
  17. 2017Neural Style Transfer: A Reviewcited by 199
  18. 2021StyTr2: Image Style Transfer with Transformerscited by 186
  19. 2021AdaAttN: Revisit Attention Mechanism in Arbitrary Neural Style Transfercited by 178
  20. 2019Style Transfer by Relaxed Optimal Transport and Self-Similaritycited by 167
  21. 2017Demystifying Neural Style Transfercited by 152
  22. 2020Contextual Residual Aggregation for Ultra High-Resolution Image Inpaintingcited by 124
  23. 2017Visual Attribute Transfer through Deep Image Analogycited by 123
  24. 2021ArtFlow: Unbiased Image Style Transfer via Reversible Neural Flowscited by 113
  25. 2024Measuring Style Similarity in Diffusion Modelscited by 106
  26. 2022Domain Enhanced Arbitrary Image Style Transfer via Contrastive Learningcited by 93
  27. 2021Aggregated Contextual Transformations for High-Resolution Image Inpaintingcited by 89
  28. 2020High-Resolution Image Inpainting with Iterative Confidence Feedback and Guided Upsamplingcited by 79
  29. 2018Arbitrary Style Transfer with Deep Feature Reshufflecited by 75
  30. 2020Arbitrary Style Transfer via Multi-Adaptation Networkcited by 70
  31. 2019PSGAN: Pose and Expression Robust Spatial-Aware GAN for Customizable Makeup Transfercited by 66
  32. 2021Image Inpainting via Conditional Texture and Structure Dual Generationcited by 66
  33. 2024Efficient Diffusion Model for Image Restoration by Residual Shiftingcited by 64
  34. 2019LADN: Local Adversarial Disentangling Network for Facial Makeup and De-Makeupcited by 63
  35. 2023Uni-paint: A Unified Framework for Multimodal Image Inpainting with Pretrained Diffusion Modelcited by 60
  36. 2021TransFill: Reference-guided Image Inpainting by Merging Multiple Color and Spatial Transformationscited by 48
  37. 2019Attention-aware Multi-stroke Style Transfercited by 47
  38. 2020Guidance and Evaluation: Semantic-Aware Image Inpainting for Mixed Scenescited by 43

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