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US Patent 10552667 Methods and systems for image processing

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Contents

Is a
Patent
Patent
0

Patent attributes

Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
0
Patent Number
105526670
Patent Inventor Names
Kenneth Michael Lande0
Brian Sung Lee0
Carl Davis Bogan, III0
Jacob Myles Laser0
Date of Patent
February 4, 2020
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Patent Application Number
165446730
Date Filed
August 19, 2019
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Patent Citations Received
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US Patent 11402975 Apparatus and method for providing interactive content
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US Patent 11675989 Custom functional patterns for optical barcodes
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US Patent 11695975 System and method for live web camera feed and streaming transmission with definitive online identity verification for prevention of synthetic video and photographic images
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US Patent 11748932 Controllable image generation
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US Patent 11308302 Custom functional patterns for optical barcodes
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US Patent 11308657 Methods and systems for image processing using a learning engine
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US Patent 11321597 Facial patterns for optical barcodes
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US Patent 11526712 Training method and apparatus for image fusion processing model, device, and storage medium
...
Patent Primary Examiner
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Yosef Kassa
0
Patent abstract

Systems and methods are disclosed configured to pre-train an autoencoder using images that include faces, wherein the autoencoder comprises an input layer, an encoder configured to output a latent image from a corresponding input image, and a decoder configured to attempt to reconstruct the input image from the latent image. An image sequence of a CGI sculpted and textured face exhibiting a plurality of facial expressions and transitions between facial expressions is accessed. Images of the plurality of facial expressions and transitions between facial expressions are captured from a plurality of different angles. The pre-trained autoencoder is trained using source images that include a CGI face with different facial expressions captured at different angles, and using destination images that include a real face. The trained autoencoder is used to generate an output where the real face in the destination images is swapped with the CGI face, while preserving expressions of the real face.

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