Adobe, Inc.

Sven Olsen

Shabnam Ghadar

Baldo Antonio Faieta

Ratheesh Kalarot

Akhilesh Kumar

Jingwan Lu

Holger Winnemoeller

Cameron Smith

Christian Thomas Cantrell

Wei-An Lin

Wilson Chan

Archie Samuel Bagnall

Patrick James Hebron

Piotr Walczyszyn

Hui Qu

Siavash Khodadadeh

Anjali Agarwal

Systems and methods for image processing are provided. Embodiments include identifying an image of a face that includes an artifact in a part of the face. A machine learning model generates an intermediate image based on the original image. The intermediate image depicts the part of the face in a closed position. Then the model generates a corrected image based on the intermediate image. The corrected image depicts the face with the part of the face in an open position and without the artifact.

Repairing irregularities in computer-generated images

The present disclosure describes systems, methods, and non-transitory computer readable media for detecting user interactions to edit a digital image from a client device and modify the digital image for the client device by using a web-based intermediary that modifies a latent vector of the digital image and an image modification neural network to generate a modified digital image from the modified latent vector. In response to user interaction to modify a digital image, for instance, the disclosed systems modify a latent vector extracted from the digital image to reflect the requested modification. The disclosed systems further use a latent vector stream renderer (as an intermediary device) to generate an image delta that indicates a difference between the digital image and the modified digital image. The disclosed systems then provide the image delta as part of a digital stream to a client device to quickly render the modified digital image.

Web-based digital image editing in real time utilizing a latent vector stream renderer and an image modification neural network

The present disclosure relates to systems, methods, and non-transitory computer readable media for remotely generating modified digital images utilizing an interactive image editing architecture. For example, the disclosed systems receive an image editing request for remotely editing a digital image utilizing an interactive image editing architecture. In some cases, the disclosed systems maintain, via a canvas worker container, a digital stream that reflects versions of the digital image. The disclosed systems determine, from the digital stream utilizing the canvas worker container, an image differential metric indicating a difference between a first version of the digital image and a second version of the digital image associated with the image editing request. Further, the disclosed systems provide the image differential metric to a client device for rendering the second version of the digital image to reflect a modification corresponding to the user interaction.

Interactive remote digital image editing utilizing a scalable containerized architecture

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Victoria, Canada

Sven Olsen