None of These Faces Are Real! | Summary and Q&A

January 27, 2019
Two Minute Papers
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None of These Faces Are Real!


NVIDIA researchers have developed an impressive technique to generate high-quality images of non-existent celebrities using a progressive method, allowing for control and customization of features.

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Key Insights

  • ✋ NVIDIA's progressive technique generates high-resolution images of imaginary celebrities through continuous refinement.
  • 😫 The algorithm demonstrates true learning by creating novel human beings rather than copying the training set.
  • 👻 Control and customization are prioritized, allowing users to lock in specific aspects and vary parameters individually.
  • ⚧️ The algorithm can blend hairstyles, colors, morph genders, and create realistic and consistent images.
  • 🪪 It can be applied to various datasets, including cars, bedrooms, and cats, showcasing its flexibility.
  • 👾 The background behind characters can also be modified, which is a distinctive feature of latent-space based techniques.
  • ⌛ NVIDIA's advancements in graphics cards with increased memory contribute to improving image resolution in real time.


Dear Fellow Scholars, this is Two Minute Papers with Károly Zsolnai-Fehér. Before we start, I will tell you right away to hold on to your papers. When I’ve first seen the results I didn’t do that and almost fell out of the chair. Scientists at NVIDIA published an amazing work not so long ago that was able to dream up high-resolution images of imagi... Read More

Questions & Answers

Q: How does NVIDIA's progressive technique generate high-resolution images of imaginary celebrities?

The technique starts with a low-fidelity image and continuously refines it, resulting in high-quality, lifelike images of non-existent people. This is achieved through deep learning and true learning, rather than simply copying the training set.

Q: How does the algorithm allow for control and customization of generated images?

NVIDIA's algorithm enables users to lock in specific aspects such as gender, age, and pose from source images. It then fuses these aspects with another source image, creating high-fidelity images with desired characteristics.

Q: Can the algorithm provide fine-grained control over outputs?

Yes, users can vary parameters individually, allowing for more precise artistic control over the generated images. This feature enables the blending of hairstyles, colors, and even morphing between different genders.

Q: Is the algorithm limited to creating human faces?

No, the algorithm can also work on other datasets such as cars, bedrooms, and even cats. It can modify the background behind the characters, showcasing the versatility of latent-space based techniques.

Summary & Key Takeaways

  • NVIDIA has developed a progressive technique for generating high-resolution images of imaginary celebrities.

  • The algorithm can learn the properties and features of a human face to create truly novel individuals.

  • The new features of the algorithm include the ability to combine different aspects of images, fine-grained control over outputs, and interpolation between desired images.

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