20230162320. AGILEGAN-BASED REFINEMENT METHOD AND FRAMEWORK FOR CONSISTENT TEXTURE GENERATION simplified abstract (Lemon Inc.)

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AGILEGAN-BASED REFINEMENT METHOD AND FRAMEWORK FOR CONSISTENT TEXTURE GENERATION

Organization Name

Lemon Inc.

Inventor(s)

Guoxian Song of Singapore (SG)

Jing Liu of Los Angeles CA (US)

Chunpong Lai of Los Angeles CA (US)

Linjie Luo of Los Angeles CA (US)

AGILEGAN-BASED REFINEMENT METHOD AND FRAMEWORK FOR CONSISTENT TEXTURE GENERATION - A simplified explanation of the abstract

This abstract first appeared for US patent application 20230162320 titled 'AGILEGAN-BASED REFINEMENT METHOD AND FRAMEWORK FOR CONSISTENT TEXTURE GENERATION

Simplified Explanation

The patent application describes methods and systems for generating a texturized image using an input image and an exemplar texture image. The process involves generating latent code vector representations based on the input and exemplar images, blending these representations, and generating a texturized image using a generative adversarial network (GAN) generator.

  • The system receives an input image and an exemplar texture image.
  • An encoder generates a latent code vector representation based on the input image.
  • A GAN generator generates a second latent code vector representation based on the exemplar texture image.
  • The first and second latent code vector representations are blended to obtain a blended latent code vector representation.
  • The GAN generator then generates a texturized image based on the blended latent code vector representation.
  • The texturized image is provided as the output image.

Potential Applications

  • Image editing and enhancement: This technology can be used to add textures to images, enhancing their visual appeal.
  • Virtual reality and gaming: Texturized images can be used to create realistic and immersive virtual environments.
  • Graphic design and art: Artists and designers can use this technology to create unique and visually interesting textures for their work.

Problems Solved

  • Lack of efficient methods for generating texturized images: This technology provides a systematic approach for generating texturized images by blending latent code vector representations.
  • Difficulty in adding textures to images: The system simplifies the process of adding textures to images by automatically generating the texturized image based on the input and exemplar images.

Benefits

  • Enhanced visual appeal: Texturized images can add depth and interest to visual content, making it more engaging for viewers.
  • Time-saving: The automated process of generating texturized images eliminates the need for manual editing, saving time and effort.
  • Versatility: The system can work with various input and exemplar images, allowing for a wide range of texturization possibilities.


Original Abstract Submitted

methods and systems for generating a texturized image are disclosed. some examples may include: receiving an input image, receiving an exemplar texture image, generating, using an encoder, a first latent code vector representation based on the input image, generating, using a generative adversarial network generator, a second latent code vector representation based on the exemplar texture image, blending the first latent code vector representation and the second latent code vector representation to obtain a blended latent code vector representation, generating, by the gan generator, a texturized image based on the blended latent code vector representation and providing the texturized image as an output image.

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