yuval-alaluf/SAM

• highreadme#1

  Clarify repo name in README H1 to avoid collision with Meta's SAM

  Why:

  CURRENT

  # Only a Matter of Style: Age Transformation Using a Style-Based Regression Model (SIGGRAPH 2021)

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  # SAM (Style-Based Age Transformation Model): Only a Matter of Style: Age Transformation Using a Style-Based Regression Model (SIGGRAPH 2021) - *Note: This project is not related to Meta's Segment Anything Model (SAM).*

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• mediumreadme#2

  Add a concise, application-focused sentence to the README's opening

  Why:

  CURRENT

  The task of age transformation illustrates the change of an individual's appearance over time. Accurately modeling this complex transformation over an input facial image is extremely challenging as it requires making convincing and possibly large changes to facial features and head shape, while still preserving the input identity. In this work, we present an image-to-image translation method that learns to directly encode real facial images into the latent space of a pre-trained unconditional GAN (e.g., StyleGAN) subject to a given aging shift. We employ a pre-trained age regression network used to explicitly guide the encoder to generate the latent codes corresponding to the desired age. In this formulation, our method approaches the continuous aging process as a regression task between the input age and desired target age, providing fine-grained control on the generated image. Moreover, unlike other approaches that operate solely in the latent space using a prior on the path controlling age, our method learns a more disentangled, non-linear path. We demonstrate that the end-to-end nature of our approach, coupled with the rich semantic latent space of StyleGAN, allows for further editing of the generated images. Qualitative and quantitative evaluations show the advantages of our method compared to state-of-the-art approaches.

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  This repository provides the official implementation for SAM, a state-of-the-art model for generating realistic age progression and regression on human faces using StyleGAN. The task of age transformation illustrates the change of an individual's appearance over time. Accurately modeling this complex transformation over an input facial image is extremely challenging as it requires making convincing and possibly large changes to facial features and head shape, while still preserving the input identity. In this work, we present an image-to-image translation method that learns to directly encode real facial images into the latent space of a pre-trained unconditional GAN (e.g., StyleGAN) subject to a given aging shift. We employ a pre-trained age regression network used to explicitly guide the encoder to generate the latent codes corresponding to the desired age. In this formulation, our method approaches the continuous aging process as a regression task between the input age and desired target age, providing fine-grained control on the generated image. Moreover, unlike other approaches that operate solely in the latent space using a prior on the path controlling age, our method learns a more disentangled, non-linear path. We demonstrate that the end-to-end nature of our approach, coupled with the rich semantic latent space of StyleGAN, allows for further editing of the generated images. Qualitative and quantitative evaluations show the advantages of our method compared to state-of-the-art approaches.

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• lowtopics#3

  Expand repository topics with more specific terms

  Why:

  CURRENT

  age-transformation, aging, generative-adversarial-networks, stylegan

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  age-transformation, aging, generative-adversarial-networks, stylegan, face-aging, face-generation, image-to-image-translation, computer-vision

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Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

• CATEGORY QUERY

  How can I generate realistic age progression and regression for human faces?

  you: not recommended
  AI recommended (in order):

  1. Artbreeder
  2. FaceApp
  3. DeepFaceLab (iperov/DeepFaceLab)
  4. StyleGAN2/StyleGAN3 (NVlabs/stylegan3)
  5. Pytorch-GAN
  6. OpenCV (opencv/opencv)

  AI recommended 6 alternatives but never named yuval-alaluf/SAM. This is the gap to close.

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• CATEGORY QUERY

  What models provide fine-grained control for age transformation using generative networks?

  you: not recommended
  AI recommended (in order):

  1. StyleGAN
  2. InterFaceGAN
  3. StyleFlow
  4. GANSpace
  5. Age-cGAN
  6. AG-GAN
  7. StarGAN v2
  8. Stable Diffusion
  9. DALL-E 3
  10. CARA
  11. FPA-GAN

  AI recommended 11 alternatives but never named yuval-alaluf/SAM. This is the gap to close.

  Show full AI answer

• Metadata completeness

  pass

• README presence

  pass

• Compared to common alternatives in this category, what is the core differentiator of yuval-alaluf/SAM?

  pass
  ✓AI named yuval-alaluf/SAM explicitly

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

• If a team adopts yuval-alaluf/SAM in production, what risks or prerequisites should they evaluate first?

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  ✓AI named yuval-alaluf/SAM explicitly

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

• In one sentence, what problem does the repo yuval-alaluf/SAM solve, and who is the primary audience?

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  ✓AI named yuval-alaluf/SAM explicitly

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?