COLORIZING VISUAL CONTENT USING ARTIFICIAL INTELLIGENCE MODELS

Number: 20250238974
Published: 2025-07-24
Filed: 2025-01-23
Assignee: DISNEY ENTERPRISES, INC.
Inventors: DJELOUAH; Abdelaziz et al.
CPC: G06T11/10
Verdict: Low Notable software
Source: Google Patents · FreePatentsOnline

The keeper's note

AI-based visual-content colorization creative-ML technique.

Abstract

Embodiments of the present disclosure provide techniques for colorizing visual content using artificial intelligence models. An example method generally includes receiving an image and an input prompt specifying a colorization to apply to the image. Based on an encoded version of the image and a textual description of the image input into a machine learning model, one or more color maps associated with the specified colorization to apply to the image are generated. A colorized version of the image is generated by a generative artificial intelligence model based on combining a grayscale version of the image and the one or more color maps, and the colorized version of the image is output.

Background

BACKGROUND Field of the Various Embodiments

Embodiments of the present disclosure relate generally to computer vision and machine learning and, more specifically, to techniques for image and video colorization using artificial intelligence models. DESCRIPTION OF THE RELATED ART

Colorizing visual content (e.g., images or video content) is a common problem in image restoration. Colorizing visual content may be performed for artistic purposes (e.g., to change the coloration of visual content), in restoring visual content captured in monochrome or with faded colors, and the like.

Various techniques exist for colorizing visual content. For example, the use of color hints in the context of colorizing a single image or multiple frames in video content can be used to colorize visual content using a transformer model or other generative artificial intelligence models. In automatic colorization models, a convolutional neural network may be used to convert a colorization task to an object classification task, or transformer models can be used for image colorization. Generally, automatic colorization models may output a single or limited range of colorization tasks. Additionally, colorization models may not apply a correct or consistent colorization across an object (e.g., may apply different colors or different shades of the same color to different surfaces of the object).

Video colorization techniques may impose additional complexities in colorizing visual c

Claims

1. A processor-implemented method, comprising: receiving an image and an input prompt specifying a colorization to apply to the image; generating, based on an encoded version of the image and a textual description of the image input into a machine learning model, one or more color maps associated with the specified colorization to apply to the image; generating, by the machine learning model, a colorized version of the image based on combining a greyscale version of the image and the one or more color maps; and outputting the colorized version of the image. || 12. A processor-implemented method, comprising: receiving a training data set of color images and corresponding greyscale images; encoding the color images and the corresponding greyscale images into a latent space; training a generative model to generate an image based on the encoded color images and the encoded greyscale images; and deploying the trained generative model. || 0. || 17. A processing system, comprising: at least one memory having executable instructions stored thereon; and one or more processors configured to execute the executable instructions to cause the processing system to: receive an image and an input prompt specifying a colorization to apply to the image; generate, based on an encoded version of the image and a textual description of the image input into a machine learning model, one or more color maps associated with the specified colorization to apply to the image; generate, by the machine learning model, a colorized version of the image based on combining a greyscale version of the image and the one or more color maps; and output the colorized version of the image.