Abstract: A mobile application with an improved user interface facilitates generating stylized media content items including images and videos. An end-user selects a desired visual effect from a set of options. The mobile application captures or accesses an image. The image is processed on a server using a generative neural network pre-trained to apply stylizations based on the selected effect. The server sends back the stylized image to the mobile application for display. The end-user can then save the stylized image or generate a video (e.g., an animation) showing the original image transition to the stylized image. The user interface provides an efficient creative workflow to apply aesthetic enhancements in a visual style chosen by the end-user. Generative machine learning techniques automate stylization to enable accessible media customization and sharing.

Abstract: A system and method for generating augmented reality (AR) experiences are disclosed. The system generates source and target indications associated with an image transformation, and generates a first set of source images and first set of target images using a first trained machine learning (ML) model, the source indications, and the target indications. The system trains a second ML model to generate a target image corresponding to a source image based on the first set of source images and the first set of target images, and generates a second set of target images using the second trained ML model and a second set of source images. The system trains a third ML model to generate an additional target image corresponding to an additional source image based on the second set of source images and second set of target images, and generates an AR experience comprising the third trained ML model.

Abstract: A mobile application with an improved user interface facilitates generating stylized media content items including images and videos. An end-user selects a desired visual effect from a set of options. The mobile application captures or accesses an image. The image is processed on a server using a generative neural network pre-trained to apply stylizations based on the selected effect. The server sends back the stylized image to the mobile application for display. The end-user can then save the stylized image or generate a video (e.g., an animation) showing the original image transition to the stylized image. The user interface provides an efficient creative workflow to apply aesthetic enhancements in a visual style chosen by the end-user. Generative machine learning techniques automate stylization to enable accessible media customization and sharing.

Abstract: A mobile application with an improved user interface facilitates generating stylized media content items including images and videos. An end-user selects a desired visual effect from a set of options. The mobile application captures or accesses an image. The image is processed on a server using a generative neural network pre-trained to apply stylizations based on the selected effect. The server sends back the stylized image to the mobile application for display. The end-user can then save the stylized image or generate a video (e.g., an animation) showing the original image transition to the stylized image. The user interface provides an efficient creative workflow to apply aesthetic enhancements in a visual style chosen by the end-user. Generative machine learning techniques automate stylization to enable accessible media customization and sharing.