Snap Inc.

Sergey Smetanin

Roman Golobokov

Nikita Demidov

Nikita Belosludtcev

Pavel Savchenkov

Ivan Babanin

Dor Ayalon

Timur Zakirov

Vladimir Gordienko

Arnab Ghosh

Anna Kovalenko

Aleksandr Larionov

Aleksandr Zakharov

Sofya Savinova

Erin Houston

Jeremy Baker Voss

Daniel Moreno

David Phillip Taitz

Sergey Tulyakov

Jian Ren

Mariya Apanovych

Mykyta Bakunov

Emily Ann Claudet

Yuelin Cheng

Hee Hun Kim

Aliaksei Mikhailiuk

Neha Yadav

Bingqian Lu

Tianxiang Gao

Examples described herein relate to techniques for facilitating selection of stickers for inclusion in messages within the context of an interaction system. According to some examples, message content is detected and a set of candidate stickers is identified based on the message content. A search icon is dynamically replaced with a representation of respective ones of the set of candidate stickers. At a first point in time, the search icon represents a first candidate sticker of the set of candidate stickers. At a second point in time, the search icon represents a second candidate sticker of the set of candidate stickers.

Sticker search icon providing dynamic previews

Examples disclosed herein describe techniques for automatic image quality evaluation. A first set of images generated by a first automated image generator and a second set of images generated by a second automated image generator are accessed. A first machine learning model generates a first quality indicator for each image in the first set of images and the second set of images. A second machine learning model generates a second quality indicator for each image in the first set of images and the second set of images. Based on the generated indicators, a first image from the first set of images and a second image from the second set of images are automatically selected and compared. A first ranking of the first automated image generator and the second automated image generator is generated based on the comparison, and ranking data is caused to be presented on a device.

Automatic image quality evaluation

Described is a system for generating a textual response from a received image by determining participation in an interaction function by a first user of an interaction system, identifying an image associated with the participation, processing data associated with the image using a first machine learning model to identify one or more features within the image, and generating a prompt based on the identified one or more features. The system then identifying instructions for a second machine learning model, processing the prompt and the instructions using the second machine learning model to generate a textual response to the image, and causing display of the textual response within the interaction function to the first user.

Image-to-text large language models (LLM)

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

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

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

Examples disclosed herein describe prompt modification techniques for automated image generation. An image generation request comprising a base prompt is received from a user device. A plurality of prompt modifiers is identified. A processor-implemented scoring engine determines, for each prompt modifier, a modifier score. The modifier score for each prompt modifier is associated with the base prompt. One or more of the prompt modifiers are automatically selected based on the modifier scores. A modified prompt is generated. The modified prompt is based on the base prompt and the one or more selected prompt modifiers. The modified prompt is provided as input to an automated image generator to generate an image, and the image is caused to be presented on the user device.

Prompt modification for automated image generation

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

Sergey Smetanin