Snap Inc.

Patrick Poirson

Maksim Gusarov

Kevin Sarabia Dela Rosa

Chen Wang

Adel Elmalaha

Kwot Sin Lee

Zhenpeng Zhou

Oleg Tovstyi

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.

Embeddings representing visual augmentations

Aspects of the present disclosure involve a system comprising a computer-readable storage medium storing a program and a method for performing operations comprising: receiving an image from a client device; applying a machine learning model to the image to generate an embedding query vector, the machine learning model being trained to encode a plurality of images and text into a common embedding space; searching, based on the embedding query vector, a database of augmented reality (AR) experiences to identify a subset of AR experiences associated with one or more embeddings that correspond to the embedding query vector; and transmitting to the client device the subset of AR experiences associated with the one or more embeddings that correspond to the embedding query vector.

Searching augmented reality experiences using visual embeddings

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Gilbert, AZ, United States of America

Patrick Poirson