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US Patent 11875221 Attribute decorrelation techniques for image editing

Patent 11875221 was granted and assigned to Adobe Inc. on January, 2024 by the United States Patent and Trademark Office.

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Is a
Patent
Patent
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Patent attributes

Patent Applicant
Adobe Inc.
Adobe Inc.
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Current Assignee
Adobe Inc.
Adobe Inc.
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Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
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Patent Number
118752210
Patent Inventor Names
Jun-Yan Zhu0
Niloy Mitra0
Wei-An Lin0
Shabnam Ghadar0
Ratheesh Kalarot0
Baldo Faieta0
Zhixin Shu0
Elya Shechtman0
...
Date of Patent
January 16, 2024
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Patent Application Number
174684760
Date Filed
September 7, 2021
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Patent Citations
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US Patent 11227187 Generating artificial intelligence solutions using raw data and simulated data
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US Patent 11354792 System and methods for modeling creation workflows
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US Patent 11494667 Systems and methods for improved adversarial training of machine-learned models
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US Patent 11521362 Messaging system with neural hair rendering
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US Patent 11544880 Generating modified digital images utilizing a global and spatial autoencoder
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US Patent 11557022 Neural network system with temporal feedback for denoising of rendered sequences
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US Patent 11580673 Methods, systems, and computer readable media for mask embedding for realistic high-resolution image synthesis
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US Patent 11610435 Generative adversarial neural network assisted video compression and broadcast
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Patent Citations Received
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US Patent 11983628 Attribute control techniques for image editing
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Patent Primary Examiner
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Andrae S Allison
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CPC Code
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G06T 2207/20081
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G06T 2207/20084
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G06T 2207/20221
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G06T 2210/22
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G06T 11/00
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G06V 10/82
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G06V 40/168
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G06N 3/08
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Patent abstract

Systems and methods generate a filtering function for editing an image with reduced attribute correlation. An image editing system groups training data into bins according to a distribution of a target attribute. For each bin, the system samples a subset of the training data based on a pre-determined target distribution of a set of additional attributes in the training data. The system identifies a direction in the sampled training data corresponding to the distribution of the target attribute to generate a filtering vector for modifying the target attribute in an input image, obtains a latent space representation of an input image, applies the filtering vector to the latent space representation of the input image to generate a filtered latent space representation of the input image, and provides the filtered latent space representation as input to a neural network to generate an output image with a modification to the target attribute.

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