mlsanityData to 7 Oct 2026

MyStyle++: A Controllable Personalized Generative Prior

arXiv 2306.04865 ↗First posted 8 Jun 2023cs.CVcited by 8

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Abstract (arXiv)

In this paper, we propose an approach to obtain a personalized generative prior with explicit control over a set of attributes. We build upon MyStyle, a recently introduced method, that tunes the weights of a pre-trained StyleGAN face generator on a few images of an individual. This system allows synthesizing, editing, and enhancing images of the target individual with high fidelity to their facial features. However, MyStyle does not demonstrate precise control over the attributes of the generated images. We propose to address this problem through a novel optimization system that organizes the latent space in addition to tuning the generator. Our key contribution is to formulate a loss that arranges the latent codes, corresponding to the input images, along a set of specific directions according to their attributes. We demonstrate that our approach, dubbed MyStyle++, is able to synthesize, edit, and enhance images of an individual with great control over the attributes, while preserving the unique facial characteristics of that individual.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 35 of its 45 references that are in the map. First posted on arXiv 8 Jun 2023.

Builds on

35 of 45 references.

References · 35

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
  3. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 5,986
  4. 2019Analyzing and Improving the Image Quality of StyleGANcited by 3,580
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  11. 2017Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networkscited by 707
  12. 2020GANSpace: Discovering Interpretable GAN Controlscited by 612
23 more
  1. 2021Designing an Encoder for StyleGAN Image Manipulationcited by 542
  2. 2020One-Shot Free-View Neural Talking-Head Synthesis for Video Conferencingcited by 438
  3. 2021Pivotal Tuning for Latent-based Editing of Real Imagescited by 427
  4. 2020InterFaceGAN: Interpreting the Disentangled Face Representation Learned by GANscited by 393
  5. 2020StyleFlow: Attribute-conditioned Exploration of StyleGAN-Generated Images using Conditional Continuous Normalizing Flowscited by 348
  6. 2020StyleSpace Analysis: Disentangled Controls for StyleGAN Image Generationcited by 331
  7. 2019Few-Shot Adversarial Learning of Realistic Neural Talking Head Modelscited by 284
  8. 2020StyleRig: Rigging StyleGAN for 3D Control over Portrait Imagescited by 267
  9. 2019Few-Shot Unsupervised Image-to-Image Translationcited by 222
  10. 2021GRAM: Generative Radiance Manifolds for 3D-Aware Image Generationcited by 216
  11. 2018Talking Face Generation by Adversarially Disentangled Audio-Visual Representationcited by 212
  12. 2021Few-shot Image Generation via Cross-domain Correspondencecited by 206
  13. 2021High-Fidelity GAN Inversion for Image Attribute Editingcited by 197
  14. 2019Few-shot Video-to-Video Synthesiscited by 196
  15. 2021StyleGAN-NADA: CLIP-Guided Domain Adaptation of Image Generatorscited by 164
  16. 2022EAMM: One-Shot Emotional Talking Face via Audio-Based Emotion-Aware Motion Modelcited by 151
  17. 2022Generative Neural Articulated Radiance Fieldscited by 101
  18. 2021GAN-Control: Explicitly Controllable GANscited by 91
  19. 2020PIE: Portrait Image Embedding for Semantic Controlcited by 41
  20. 2021Sketch Your Own GANcited by 37
  21. 2022Masked Lip-Sync Prediction by Audio-Visual Contextual Exploitation in Transformerscited by 33
  22. 2021High-fidelity Face Tracking for AR/VR via Deep Lighting Adaptationcited by 13
  23. 2022LatentSwap3D: Semantic Edits on 3D Image GANscited by 5

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Cited by year

Citing papers per year: 2023: 3; 2024: 3; 2025: 2.

8 papers.