ccpl

Maintainer: jarrentwu1031

Total Score

1

Last updated 5/19/2024
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Github LinkView on Github
Paper LinkView on Arxiv

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Model overview

CCPL is a versatile style transfer model that can perform artistic, photo-realistic, and other types of image-to-image transformations. It was developed by Jarrent Wu, and was presented at the ECCV 2022 conference. CCPL builds upon previous style transfer models like AdaIN and CUT, using a novel contrastive coherence preserving loss to enable more diverse and consistent style transfers.

Model inputs and outputs

CCPL takes two inputs - a content image and a style image. The content image is the image you want to transform, while the style image provides the artistic or photographic style you want to apply. The model can generate an output image that combines the content of the input image with the style of the provided style image.

Inputs

  • Content: The image you want to transform
  • Style: The image that provides the artistic or photographic style to apply

Outputs

  • Stylized Image: The output image that combines the content of the input with the style of the provided style image

Capabilities

CCPL can perform a wide range of style transfer tasks, including artistic style transfer, photo-realistic style transfer, super-resolution, and short-term and long-term temporal consistency. The model is able to preserve important content features while effectively transferring the desired style, resulting in high-quality, coherent stylized images.

What can I use it for?

CCPL can be used for a variety of creative and practical applications, such as enhancing photos, generating artwork, and creating stylized animations. The model's versatility makes it a powerful tool for artists, designers, and content creators who want to explore new creative possibilities. Companies could also potentially use CCPL for tasks like stylizing product images or creating unique marketing visuals.

Things to try

One interesting aspect of CCPL is its ability to maintain temporal consistency when processing video frames. This can be useful for creating stylized animations or enhancing the look of existing footage. Another intriguing feature is the model's capacity for image-to-image translation, which could enable novel applications like transforming sketches into paintings or converting aerial photos into maps.



This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

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