DIFFERENTIABLE COMPOSITION OF ATTRIBUTES IN STYLE TRANSFER

Number: 20250342620
Published: 2025-11-06
Filed: 2024-05-03
Assignee: DISNEY ENTERPRISES, INC.
Inventors: ORTIZ; Raphael Francois et al.
CPC: G06T11/10
Verdict: Low Notable software
Source: Google Patents · FreePatentsOnline

The keeper's note

Differentiable attribute-composition creative-ML style-transfer technique.

Abstract

One embodiment of the present invention sets forth a technique for performing style transfer. The technique includes determining a first set of attribute values for a plurality of attributes associated with a content sample. The technique also includes computing one or more losses based on the content sample and one or more style samples and converting, based on the one or more losses, the first set of attribute values into a second set of attribute values for the plurality of attributes. The technique further includes generating a style transfer result based on a composite of the second set of attribute values.

Background

BACKGROUND Field of the Various Embodiments

Embodiments of the present disclosure relate generally to machine learning and computer vision and, more specifically, to techniques for differentiable composition of attributes in style transfer. DESCRIPTION OF THE RELATED ART

Style transfer refers to a technique for transferring the “style” of a first image onto a second image without modifying the content of the second image. For example, colors, patterns, and/or other style-based attributes of the first image may be transferred onto one or more faces, buildings, bridges, and/or other objects in the second image without removing the objects from the second image or adding new objects to the second image.

Neural style transfer (NST) refers to a category of style transfer techniques that leverage convolutional neural networks (CNNs) to perform style transfer. NST techniques typically extract features from both the content and style images using a pre-trained CNN and modify the features of the content image to match those of the style image. The modified features are then used to generate a new image that has the content of the original image and the style of the style image. For example, an encoder neural network could be used to generate feature maps for both the content and style images. A mean and standard deviation may be calculated for one or more portions of the feature map for the style image, and the corresponding portion(s) of the feature map for the c

Claims

1. A computer-implemented method for performing style transfer, the method comprising: determining a first set of attribute values for a plurality of attributes associated with a content sample; computing one or more losses based on the content sample and one or more style samples; converting, based on the one or more losses, the first set of attribute values into a second set of attribute values for the plurality of attributes; and generating a style transfer result based on a composite of the second set of attribute values. || 11. One or more non-transitory computer-readable media storing instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of: determining a first set of attribute values for a plurality of attributes associated with a content sample; computing one or more losses based on the content sample and one or more style samples; converting, based on the one or more losses, the first set of attribute values into a second set of attribute values for the plurality of attributes; and generating a style transfer result based on a composite of the second set of attribute values. || 20. A system, comprising: one or more memories that store instructions, and one or more processors that are coupled to the one or more memories and, when executing the instructions, are configured to perform the steps of: determining a first set of attribute values for a plurality of attributes associated with a content sample; computing one or more losses based on the content sample and one or more style samples; converting, based on the one or more losses, the first set of attribute values into a second set of attribute values for the plurality of attributes; and generating a style transfer result based on a composite of the second set of attribute values.