Snap Inc.

Oleg Tovstyi

Maksim Gusarov

Patrick Poirson

Chen Wang

Zhenpeng Zhou

An input video item that includes a target visual augmentation is accessed. A machine learning model uses the input video item to generate an embedding. The embedding may comprise a vector representation of a visual effect of the target visual augmentation. The machine learning model is trained, in an unsupervised training phase, to minimize loss between training video representations generated within each of a plurality of training sets. Each training set comprises a plurality of different training video items that each include a predefined visual augmentation. Based on the generation of the embedding of the input video item, the target visual augmentation is mapped to an augmentation identifier.

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Los Angeles, CA, United States of America

Oleg Tovstyi