Adobe, Inc.

Yumin Jia

Jen-Chan Jeff Chien

Xin Lu

Haiting Lin

Jinoh Oh

Wentian Zhao

Zichuan Liu

Yeojin Kim

Shitong Wang

He Qin

Gahye Park

Zhan Yu

The present disclosure describes systems, non-transitory computer-readable media, and methods for utilizing a machine learning model trained to determine subtle pose differentiations to analyze a repository of captured digital images of a particular user to automatically capture digital images portraying the user. For example, the disclosed systems can utilize a convolutional neural network to determine a pose/facial expression similarity metric between a sample digital image from a camera viewfinder stream of a client device and one or more previously captured digital images portraying the user. The disclosed systems can determine that the similarity metric satisfies a similarity threshold, and automatically capture a digital image utilizing a camera device of the client device. Thus, the disclosed systems can automatically and efficiently capture digital images, such as selfies, that accurately match previous digital images portraying a variety of unique facial expressions specific to individual users.

Capturing digital images utilizing a machine learning model trained to determine subtle pose differentiations

Embodiments are disclosed for generating lens blur effects. The disclosed systems and methods comprise receiving a request to apply a lens blur effect to an image, the request identifying an input image and a first disparity map, generating a plurality of disparity maps and a plurality of distance maps based on the first disparity map, splatting influences of pixels of the input image using a plurality of reshaped kernel gradients, gathering aggregations of the splatted influences, and determining a lens blur for a first pixel of the input image in an output image based on the gathered aggregations of the splatted influences.

Kernel reshaping-powered splatting-based efficient image space lens blur

The present disclosure relates to systems, methods, and non-transitory computer-readable media that generate refined segmentation masks for digital visual media items. For example, in one or more embodiments, the disclosed systems utilize a segmentation refinement neural network to generate an initial segmentation mask for a digital visual media item. The disclosed systems further utilize the segmentation refinement neural network to generate one or more refined segmentation masks based on uncertainly classified pixels identified from the initial segmentation mask. To illustrate, in some implementations, the disclosed systems utilize the segmentation refinement neural network to redetermine whether a set of uncertain pixels corresponds to one or more objects depicted in the digital visual media item based on low-level (e.g., local) feature values extracted from feature maps generated for the digital visual media item.

Iteratively refining segmentation masks

Utilizing a machine learning model trained to determine subtle pose differentiations to automatically capture digital images

Generating refined segmentation masks based on uncertain pixels

Facial skin mask generated by a digital content creation system is described. The digital content creation system includes digital effects on skin in facial regions of digital content with efficiency and accuracy. Upon identifying a facial region within digital content, the system generates a first regional skin mask, a second regional skin mask, and combines both of the first and second regional skin masks to generate a facial skin mask indicative of skin of the identified facial regions depicted in digital content. The digital content creation system then modifies digital content by applying user selected digital effects to the skin of the facial region using the generated facial skin mask.

Modifying digital content with digital effects using facial skin mask

This application relates generally to parallel computer processing, and more specifically, to parallel processing within a rendering engine via parallel scene graphs. One or more parallel scene graphs or parallel data graphs may be provided to a rendering engine. The rendering engine may identify dependencies within the parallel data structures and process, in parallel, one or more aspects of a the parallel data structure.

Parallel rendering engine

Growing community of inventors

San Jose, CA, United States of America

Yumin Jia