Snap Inc.

Sergei Kotcur

Maksim Gusarov

Igor Kudriashov

Sergey Tulyakov

Christopher Yale Crutchfield

Andrew Wan

Victor Shaburov

Yurii Monastyrshyn

Oleksandr Pyshchenko

Valerii Filev

Aleksei Stoliar

Jaewook Chung

Emre Yamangil

Sergei Korolev

Yanping Chen

Aymeric Damien

Theresa Barton

A system of machine learning schemes can be configured to efficiently perform image processing tasks on a user device, such as a mobile phone. The system can selectively detect and transform individual regions within each frame of a live streaming video. The system can selectively partition and toggle image effects within the live streaming video.

Device-based image modification of depicted objects

A compact generative neural network can be distilled from a teacher generative neural network using a training network. The compact network can be trained on the input data and output data of the teacher network. The training network train the student network using a discrimination layer and one or more types of losses, such as perception loss and adversarial loss.

Generative neural network distillation

Systems, devices, and methods are presented for segmenting an image of a video stream with a client device by receiving one or more images depicting an object of interest and determining pixels within the one or more images corresponding to the object of interest. The systems, devices, and methods identify a position of a portion of the object of interest and determine a direction for the portion of the object of interest. Based on the direction of the portion of the object of interest, a histogram threshold is dynamically modified for identifying pixels as corresponding to the portion of the object of interest. The portion of the object of interest is replaced with a graphical interface element aligned with the direction of the portion of the object of interest.

Object modeling and replacement in a video stream

Systems, devices, media, and methods are presented for generating facial representations using image segmentation with a client device. The systems and methods receive an image depicting a face, detect at least a portion of the face within the image, and identify a set of facial landmarks within the portion of the face. The systems and methods determine one or more characteristics representing the portion of the face, in response to detecting the portion of the face. Based on the one or more characteristics and the set of facial landmarks, the systems and methods generate a representation of a face.

Automated avatar generation

Growing community of inventors

Sochi, Russia

Sergei Kotcur