Generating cartoon images from photos

US10853987B2 · US · B2

Patent metadata
FieldValue
Publication numberUS-10853987-B2
Application numberUS-201916702440-A
CountryUS
Kind codeB2
Filing dateDec 3, 2019
Priority dateMar 20, 2017
Publication dateDec 1, 2020
Grant dateDec 1, 2020

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  1. Title

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  2. Abstract

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  5. First independent claim

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Abstract

Official abstract text for this publication.

A system and method for generating cartoon images from photos are described. The method includes receiving an image of a user, determining a template for a cartoon avatar, determining an attribute needed for the template, processing the image with a classifier trained for classifying the attribute included in the image, determining a label generated by the classifier for the attribute, determining a cartoon asset for the attribute based on the label, and rendering the cartoon avatar personifying the user using the cartoon asset.

First claim

Opening claim text (preview).

What is claimed is: 1. A computer-implemented method comprising: receiving, using one or more computing devices, an image of a user; determining, using the one or more computing devices, a template for an avatar; determining, based on the image of the user and using the one or more computing devices, attributes needed for the template, wherein the attributes include a plurality of facial attributes; processing, using the one or more computing devices, the image with a respective classifier trained to generate a corresponding label that corresponds to a value of each of the attributes, wherein each classifier is coupled to a selected layer of a pre-trained neural network model and wherein the selected layer of the pre-trained neural network model is selected by determining that the selected layer provides discrimination for a corresponding attribute; determining, using the one or more computing devices, a set of assets for the avatar based on labels; and rendering, using the one or more computing devices, the avatar personifying the user using the set of assets by: organizing each asset of the set of assets into a position relative to other assets corresponding to the plurality of facial attributes in the template; and rendering the avatar based on the organized assets. 2. The computer-implemented method of claim 1 , further comprising dividing the attributes into a primary attribute list and a secondary attribute list. 3. The computer-implemented method of claim 2 , wherein determining the set of assets based on the labels is based on ranking candidate assets associated with each label based on a level of match between each candidate asset and the label, and using the candidate assets that meet a threshold rank. 4. The computer-implemented method of claim 1 , wherein the set of assets are a first set of assets that correspond to a first style and are associated with a face parameterization and further comprising: receiving a second set of assets that correspond to a second style or emotional expression; determining whether the second style or emotional expression is compatible with the face parameterization; and responsive to determining that the second style or the emotional expression is compatible with the face parameterization, determining a new set of assets for the second style or emotional expression. 5. The computer-implemented method of claim 4 , wherein determining the new set of assets includes: mapping the first set of assets to the second style or emotional expression; and deriving the new set of assets based on the mapping. 6. The computer-implemented method of claim 1 , wherein rendering the avatar further includes adding fixed elements that are specific to a style for the avatar. 7. The computer-implemented method of claim 1 , further comprising: presenting the avatar on a display of a user device; receiving user input on the display that is a manual edit of the avatar; and rerendering the avatar based on a changed face parameterization, wherein the changed face parameterization is based on the user input. 8. A computer program product comprising a non-transitory computer readable medium including a computer readable program, wherein the computer readable program when executed on a computer causes the computer to perform operations comprising: receiving, using one or more computing devices, an image of a user; determining, using the one or more computing devices, a template for an avatar; determining, based on the image of the user and using the one or more computing devices, attributes needed for the template, wherein the attributes include a plurality of facial attributes; processing, using the one or more computing devices, the image with a respective classifier trained to generate a corresponding label that corresponds to a value of each of the attributes, wherein each classifier is coupled to a selected layer of a pre-trained neural network model and wherein the selected layer of the pre-trained neural network model is selected by determining that the selected layer provides discrimination for a corresponding attribute; determining, using the one or more computing devices, a set of assets for the avatar based on labels; and rendering, using the one or more computing devices, the avatar personifying the user using the set of assets by: organizing each asset of the set of assets into a position relative to other assets corresponding to the plurality of facial attributes in the template; and rendering the avatar based on the organized assets. 9. The computer program product of claim 8 , wherein the operations further comprise dividing the attributes into a primary attribute list and a secondary attribute list. 10. The computer program product of claim 9 , wherein determining the set of assets based on the labels is based on ranking candidate assets associated with each label based on a level of match between each candidate asset and the label, and using the candidate assets that meet a threshold rank. 11. The computer program product of claim 8 , wherein the set of assets are a first set of assets that correspond to a first style and are associated with a face parameterization and the operations further comprise: receiving a second set of assets that correspond to a second style or emotional expression; determining whether the second style or emotional expression is compatible with the face parameterization; and responsive to determining that the second style or the emotional expression is compatible with the face parameterization, determining a new set of assets for the second style or emotional expression. 12. The computer program product of claim 11 , wherein determining the new set of assets includes: mapping the first set of assets to the second style or emotional expression; and deriving the new set of assets based on the mapping. 13. The computer program product of claim 8 , wherein rendering the avatar further includes adding fixed elements that are specific to a style for the avatar. 14. The computer program product of claim 8 , wherein the operations further comprise: presenting the avatar on a display of a user device; receiving user input on the display that is a manual edit of the avatar; and rerendering the avatar based on a changed face parameterization, wherein the changed face parameterization is based on the user input. 15. A system comprising: a processor; and a memory coupled to the processor and storing instructions that, when executed, cause the processor to perform operations comprising: receiving, using one or more computing devices, an image of a user; determining, using the one or more computing devices, a template for an avatar; determining, based on the image of the user and using the one or more computing devices, attributes needed for the template, wherein the attributes include a plurality of facial attributes; processing, using the one or more computing devices, the image with a respective classifier trained to generate a corresponding label that corresponds to a value of each of the attributes, wherein each classifier is coupled to a selected layer of a pre-trained neural network model and wherein the selected layer of the pre-trained neural network model is selected by determining that the selected layer provides discrimination for a corresponding attribute; determining, using the one or more computing devices, a set of assets for the avatar based on labels; and rendering, using the one or more computing devices, the avatar personifying the user using the set of assets by: organizing each asset of the set of a

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Classifications

  • G06T13/80Primary

    Two-dimensional [2D] animation, e.g. using sprites · CPC title

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What does patent US10853987B2 cover?
A system and method for generating cartoon images from photos are described. The method includes receiving an image of a user, determining a template for a cartoon avatar, determining an attribute needed for the template, processing the image with a classifier trained for classifying the attribute included in the image, determining a label generated by the classifier for the attribute, determin…
Who is the assignee on this patent?
Google Llc
What technology area does this patent fall under?
Primary CPC classification G06T13/80. Mapped technology areas include Physics.
When was this patent published?
Publication date Tue Dec 01 2020 00:00:00 GMT+0000 (Coordinated Universal Time) (B2). Legal status and post-grant events are not shown on this page.
What related patents are in patentsdb?
We list 1 related publication on this page (citations in our corpus or others sharing the same primary CPC).