View source for 20230146676. PORTRAIT STYLIZATION FRAMEWORK TO CONTROL THE SIMILARITY BETWEEN STYLIZED PORTRAITS AND ORIGINAL PHOTO simplified abstract (Lemon Inc.)

=PORTRAIT STYLIZATION FRAMEWORK TO CONTROL THE SIMILARITY BETWEEN STYLIZED PORTRAITS AND ORIGINAL PHOTO=


==Organization Name==

[[:Category:Lemon Inc.|Lemon Inc.]]


[[Category:Lemon Inc.]]

==Inventor(s)==

[[:Category:Jing Liu of Los Angeles CA (US)|Jing Liu of Los Angeles CA (US)]][[Category:Jing Liu of Los Angeles CA (US)]]

[[:Category:Chunpong Lai of Los Angeles CA (US)|Chunpong Lai of Los Angeles CA (US)]][[Category:Chunpong Lai of Los Angeles CA (US)]]

[[:Category:Guoxian Song of Singapore (SG)|Guoxian Song of Singapore (SG)]][[Category:Guoxian Song of Singapore (SG)]]

[[:Category:Linjie Luo of Los Angeles CA (US)|Linjie Luo of Los Angeles CA (US)]][[Category:Linjie Luo of Los Angeles CA (US)]]

==PORTRAIT STYLIZATION FRAMEWORK TO CONTROL THE SIMILARITY BETWEEN STYLIZED PORTRAITS AND ORIGINAL PHOTO - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20230146676 titled 'PORTRAIT STYLIZATION FRAMEWORK TO CONTROL THE SIMILARITY BETWEEN STYLIZED PORTRAITS AND ORIGINAL PHOTO

==Simplified Explanation==

The patent application describes systems and methods for controlling the similarity between stylized portraits and an original photo. Here are the key points:

* The system receives an input image and encodes it using a variational autoencoder to generate a latent vector.
* The latent vector is blended with latent vectors representing a face in the original user portrait image.
* The resulting blended latent vector is provided to a generative adversarial network (GAN) generator to generate a controlled stylized image.
* The system allows the user to interactively determine the level of stylization vs. personalization in the resulting stylized portrait.
* In some examples, layers of the stylized GAN generator can be swapped with layers of the original GAN generator.

Potential applications of this technology:

* Personalized artwork: Users can create stylized portraits that closely resemble their original photos, allowing for a unique and personalized artistic representation.
* Social media filters: The technology can be used to develop filters that apply stylization to user portraits while maintaining a recognizable likeness.
* Digital avatars: The system can generate stylized portraits that can be used as digital avatars in various applications, such as gaming or virtual reality.

Problems solved by this technology:

* Balancing stylization and personalization: The system allows users to control the level of stylization in their portraits, ensuring that the resulting images maintain a desired resemblance to the original photo.
* Generating controlled stylized images: By blending latent vectors and swapping layers in the GAN generator, the system provides a way to generate stylized portraits while maintaining control over the output.

Benefits of this technology:

* Customization: Users have the ability to determine the level of stylization in their portraits, allowing for personalized and unique artwork.
* User-friendly: The interactive nature of the system enables users to easily adjust the stylization vs. personalization balance according to their preferences.
* Versatility: The technology can be applied to various applications, such as personalized artwork, social media filters, and digital avatars.


==Original Abstract Submitted==

systems and methods directed to controlling the similarity between stylized portraits and an original photo are described. in examples, an input image is received and encoded using a variational autoencoder to generate a latent vector. the latent vector may be blended with latent vectors that best represent a face in the original user portrait image. the resulting blended latent vector may be provided to a generative adversarial network (gan) generator to generate a controlled stylized image. in examples, one or more layers of the stylized gan generator may be swapped with one or more layers of the original gan generator. accordingly, a user can interactively determine how much stylization vs. personalization should be included in a resulting stylized portrait.



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