Snap Inc.

Roman Golobokov

Nikita Demidov

Jeremy Baker Voss

Sergey Smetanin

Aleksandr Mashrabov

Aleksei Bromot

Nikita Belosludtcev

Ivan Babanin

Dor Ayalon

Timur Zakirov

Vladimir Gordienko

Grigoriy Tkachenko

Dmitry Matov

Anna Kovalenko

Aleksandr Zakharov

Alexandr Marinenko

Erin Houston

Aleksandr Larionov

Sofya Savinova

Alina Melnyk

Daniel Moreno

David Phillip Taitz

Sergey Tulyakov

Jian Ren

Pavel Savchenkov

Mariya Apanovych

Arnab Ghosh

Emily Ann Claudet

Yuelin Cheng

The subject technology receives at least one signal from a computing device, the at least one signal comprising at least one of a current time, battery power, sensor information, or location information. The subject technology generates a digital sticker, the digital sticker including graphical content indicating information based at least in part based on the at least one signal and media content including an image of a target face, the image of the target face being modified based on at least one of sets of source pose parameters to mimic at least one of positions of a head of a source actor and at least one of facial expressions of the source actor. The subject technology provides augmented reality content for display on a computing device, the augmented reality content including the digital sticker as an overlay on at least a portion of the augmented reality content.

Facial synthesis in overlaid augmented reality content

A method includes determining participation in an interaction function by a first user of an interaction system with a second user of the interaction system. The method also includes accessing profile data of the first user, and determining, based on the profile data, whether the first user has captured or designated a first-user self-image for use in the interaction function. In response to determining that the first user has not captured or designated the first-user self-image, the method includes accessing a media content item that includes a character, identifying a head portion of the character in the media content item, replacing the head portion with a placeholder space, and displaying the media content item with the placeholder space in a user interface corresponding to the interaction function.

Generating media content items during user interaction

The technical problem of creating an augmented reality (AR) experience that, on one hand, is accessible from a camera view user interface provided with a messaging client and that, also, can perform a modification based on a previously captured image of a user, is addressed by providing an AR component. When a user, while accessing the messaging client, engages a user selectable element representing the AR component in the camera view user interface, the messaging system loads the AR component in the messaging client. The AR component comprises a target media content object, which can be animation or live action video. The loaded AR component accesses a portrait image associated with a user and modifies the target media content using the portrait image. The resulting target media content object is displayed in the camera view user interface.

Using portrait images in augmented reality components

In some examples, a method includes determining that a listing of other users is to be displayed on a user interface for a first user, accessing augmentation trigger criteria, applying the augmentation trigger criteria to user profile data, determining, based on the application of the augmentation trigger criteria to the user profile data, that a second user meet a trigger criterion of the one or more augmentation trigger criteria. The method then includes identifying at least one recommended content augmentation associated with the trigger criterion and causing display of a first selectable user interface element associated with the at least one recommended content augmentation with the listing of other users on the user interface.

Recommending relevant content augmentations based on context

The subject technology captures first image data by a computing device, the first image data comprising a target face of a target actor and facial expressions of the target actor, the facial expressions including lip movements. The subject technology generates, based at least in part on frames of a source media content, sets of source pose parameters. The subject technology receives a selection of a particular facial expression from a set of facial expressions. The subject technology generates, based at least in part on sets of source pose parameters and the selection of the particular facial expression, an output media content. The subject technology provides augmented reality content based at least in part on the output media content for display on the computing device.

Facial synthesis in augmented reality content for online communities

Examples disclosed herein describe techniques related to automated image generation in an interaction system. An image generation request is received from a first user device associated with a first user of an interaction system. The image generation request comprises a text prompt. Responsive to receiving the image generation request, an image is automatically generated by an automated text-to-image generator, based on the text prompt. The image is caused to be presented on the first user device. An indication of user input to select the image is received from the user device. Responsive to receiving the indication of the user input to select the image, the image is associated with the first user within the interaction system, and a second user of the interaction system is enabled to be presented with the image.

Automatic image generation in an interaction system

Described herein is a technique for processing a received media content item (e.g., a message), received at a messaging application of a first end-user of a messaging service, to generate a selection of some predetermined number of recommended stickers. The recommended stickers are then presented in a user interface to the first end-user, allowing the first end-user to select a sticker for use in replying to the received media content item. To generate the selection of recommended stickers, in response to receiving the media content item, the messaging application processes the media content item to identify specific attributes and characteristics (e.g., text included with the message, stickers used with the message, and other contextual metadata). The identified attributes and characteristics of the received message are then processed by a scoring model to identify the predetermined number of stickers for presenting in the reply interface as recommended reply stickers.

Techniques for recommending reply stickers

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London, United Kingdom

Roman Golobokov