Snap Inc.

Vadim Velicodnii

Sergey Demyanov

Fedor Zhdanov

Aleksei Stoliar

Aleksei Podkin

Konstantin Gudkov

Aleksei Zhuravlev

Andrey Alejandrovich Gomez Zharkov

Aspects of the present disclosure involve a system comprising a computer-readable storage medium storing a program and method for interacting with visual codes within a messaging system. The program and method provide for displaying, by a messaging application, captured image data comprising a visual code, the visual code including a custom graphic and being decodable to access a first feature of the messaging application; receiving user input selecting the visual code; displaying an updated version of the custom graphic; providing an animation which depicts the updated version of the custom graphic as moving from the visual code to an interface element comprising a group of icons, each icon within the group of icons being user-selectable to access a respective second feature of the messaging application; and updating the group of icons to include an additional icon which is user-selectable to access the first feature of the messaging application.

Interacting with visual codes within messaging system

Systems and methods herein describe an image stylization system. The image stylization system accesses a set of images corresponding to a target domain style, generates a set of paired images using a first machine learning model, analyze the generated set of paired images using a second machine learning model trained to analyze the generated set of paired images based on a plurality of protected feature criteria, determines a set of image transformations for the generated set of pairs, generates a transformed set of paired images by performing the set of image transformations on the set of paired images, and generates stylized images corresponding to the target domain style using a supervised image translation model trained on the transformed set of paired images.

Protecting image features in stylized representations of a source image

Methods and systems are disclosed for performing automatically creating AR experiences on a messaging platform. The methods and systems perform operations that include: receiving, via a graphical user interface (GUI), input that specifies a plurality of image transformation parameters; accessing a set of sample source images; modifying the set of sample source images based on the plurality of image transformation parameters to generate a set of sample target images; training a machine learning model to generate a given target image from a given source image by establishing a relationship between the set of sample source images and the set of sample target images; and automatically generating an augmented reality experience comprising the trained machine learning model.

Automated augmented reality experience creation based on sample source and target images

A data item is identified on a device. A neural network that includes an adversarial transformation subnetwork is applied to the data item to generate a modified data item. Output indicative of the modified data item is caused to be presented on the device. The neural network further comprises an encoder and a decoder. The neural network is trained in at least two stages. At least one of the encoder and the decoder is trained in a first stage and the adversarial transformation subnetwork is trained in a second stage.

Deep feature generative adversarial neural networks

A mobile device can implement a neural network-based domain transfer scheme to modify an image in a first domain appearance to a second domain appearance. The domain transfer scheme can be configured to detect an object in the image, apply an effect to the image, and blend the image using color space adjustments and blending schemes to generate a realistic result image. The domain transfer scheme can further be configured to efficiently execute on the constrained device by removing operational layers based on resources available on the mobile device.

Growing community of inventors

London, United Kingdom

Vadim Velicodnii