Snap Inc.

Anna Kovalenko

Roman Golobokov

Sergey Smetanin

Nikita Demidov

Ivan Babanin

Nikita Belosludtcev

Dor Ayalon

Timur Zakirov

Vladimir Gordienko

Aleksandr Larionov

Erin Houston

Menglei Chai

Pavel Savchenkov

Aleksandr Belskikh

Emily Ann Claudet

Antoine Chassang

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

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

Embodiments enable virtual hair generation. The virtual hair generation can be performed by generating a first image of a face using a GAN model, applying 3D virtual hair on the first image to generate a second image with 3D virtual hair, projecting the second image with 3D virtual hair into a GAN latent space to generate a third image with virtual hair, performing a blend of the virtual hair with the first image of the face to generate a new image with new virtual hair that corresponds to the 3D virtual hair, training a neural network that receives the second image with the 3D virtual hair and provides an output image with virtual hair, and generating using the trained neural network, a particular output image with hair based on a particular input image with 3D virtual hair.

Generating virtual hairstyle using latent space projectors

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London, United Kingdom

Anna Kovalenko