US 12,755,349 B1Granted

Graphically altering digital media by embedding virtual objects using artificial intelligence

AssigneeWear It AI, Inc.

InventorsYan Mui Kitty Yeung, Desheng Cui

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Abstract

The systems and methods disclosed herein dynamically generate, in real-time or near real-time, media (e.g., images, video) integrating user-generated content with embedded product information using universal (i.e., non-user specific) model(s). Products (e.g., clothes) embedded in retailer content (e.g., on a person on a product webpage) can be displayed on a user (e.g., a virtual try-on) in the visual style designed by the product maker (e.g. brand's photography background and scene). The system can generate a prompt by extracting an image description using an image-to-text artificial intelligence model and combining it with a user's description. Using the prompt, the system can generate the combined image and apply a series of fine-tuning models. To realistically depict a user's shape, the system can redraw the user's outline and map the combined image to the outline. To realistically depict a user's face shape/complexion, the system can adjust the combined image in accordance with the user's outline (i.e., adjusting for face shape) and input image (i.e., adjusting for complexion).

Claims20

Dependent claims

A computer-implemented method for graphically altering digital media by embedding virtual objects using artificial intelligence (AI), the method comprising:

receiving an output generation request from a user interface of a computing device to generate an augmented image using an input, the input identifying (1) a person image and (2) a product image;
generating, using a first set of AI models, a set of input captions comprising (1) a person image caption defining a person from the person image and (2) a product image caption defining a product from the product image;
constructing a prompt of the output generation request by mapping, using a second set of AI models, (1) the person image caption and (2) the product image into a vector space to generate (1) a person vector representation and (2) a product vector representation;
generating, using a third set of AI models, an initial augmented image indicating the person wearing the product from the product image by:
generating a vector representation of a noisy image by applying a noise value to the person vector representation, wherein the noise value is indicative of an unmodified portion of the initial augmented image, and
iteratively denoising the vector representation of the noisy image in accordance with the constructed prompt to generate a vector representation of the initial augmented image,
wherein the initial augmented image depicts the person from the person image with one or more pixels of the person image replaced by the product image;
and
applying a fourth set of AI models conditioned on the vector representation of the initial augmented image to generate a fine-tuned augmented image that modifies one or more pixels of the initial augmented image in accordance with at least one of:
(1) a complexion of the person, (2) a face shape of the person, (3) a body shape of the person, (4) a pose of the person, or (5) a set of camera parameters of a camera capturing the person image;
and
causing display of, on the user interface of the computing device, graphical data representing the fine-tuned augmented image.

9 more elements

A non-transitory, computer-readable storage medium storing instructions for dynamically generating augmented digital media representing embedded product data using artificial intelligence (AI) models, wherein the instructions when executed by at least one data processor of a system, cause the system to:

receive an output generation request from a user interface of a computing device to generate an augmented image using an input, the input identifying (1) a person image and (2) a product image;
generate, using a first set of AI models, a set of input descriptions comprising (1) a person image description defining a person from the person image and (2) a product image description defining a product from the product image;
construct a prompt of the output generation request by mapping, using a second set of AI models, (1) the person image description and (2) the product image into a vector space to generate (1) a person vector representation and (2) a product vector representation;
generate, using a third set of AI models, an initial augmented image indicating the person wearing the product from the product image by:
generating a vector representation of a noisy image by applying a noise value to the person vector representation, wherein the noise value is indicative of an unmodified portion of the initial augmented image, and
iteratively denoising the vector representation of the noisy image in accordance with the constructed prompt to generate a vector representation of the initial augmented image,
wherein the initial augmented image depicts the person from the person image with one or more pixels of the person image replaced by the product image;
and
apply a fourth set of AI models conditioned on the vector representation of the initial augmented image to generate a fine-tuned augmented image that modifies one or more pixels of the initial augmented image in accordance with at least one of:
(1) a complexion of the person, (2) a face shape of the person, (3) a body shape of the person, (4) a pose of the person, or (5) a set of camera parameters of a camera capturing the person image;
and
cause display of, on the user interface of the computing device, graphical data representing the fine-tuned augmented image.

9 more elements

A system comprising:

at least one hardware processor;
and
at least one non-transitory memory storing instructions, which, when executed by the at least one hardware processor, cause the system to:
receive an output generation request from a user interface of a computing device to generate an augmented image using an input, the input identifying (1) a person image and (2) a product image;
generate, using a first set of AI models, a set of input descriptions comprising (1) a person image description defining a person from the person image and (2) a product image description defining a product from the product image;
construct a prompt of the output generation request by mapping, using a second set of AI models, (1) the person image description and (2) the product image into a vector space to generate (1) a person vector representation and (2) a product vector representation;
generate, using a third set of AI models, an initial augmented image indicating the person wearing the product from the product image by:
generating a vector representation of a noisy image by applying a noise value to the person vector representation, wherein the noise value is indicative of an unmodified portion of the initial augmented image, and
iteratively denoising the vector representation of the noisy image in accordance with the constructed prompt to generate a vector representation of the initial augmented image,
wherein the initial augmented image depicts the person from the person image with one or more pixels of the person image replaced by the product image;
and
apply a fourth set of AI models conditioned on the vector representation of the initial augmented image to generate a fine-tuned augmented image that modifies one or more pixels of the initial augmented image in accordance with at least one of:
(1) a complexion of the person, (2) a face shape of the person, (3) a body shape of the person, (4) a pose of the person, or (5) a set of camera parameters of a camera capturing the person image;
and
cause display of, on the user interface of the computing device, graphical data representing the fine-tuned augmented image.

12 more elements

Dependencies are inferred from the claim text.

Facts

Application: 19/658,369Filed 24 Apr 2026 · us provisional application
Priority: 25 Apr 2025
Granted: 6 Oct 2026
Term ends: Not estimatedThe source records don’t support an estimate.

Classification8 codes

G06T 5/60
Image enhancement or restoration: using machine learning, e.g. neural networks
G06N 3/0455
Combinations of networks: auto-encoder networks; Encoder-decoder networks
G06T 5/70
Image enhancement or restoration: denoising; Smoothing
G06T 2200/24
Indexing scheme for image data processing or generation, in general: involving graphical user interfaces [GUIs]