Abstract: Example embodiments of the present disclosure provide for an example method including generating media assets based on a pre-existing media asset. The method includes extracting signals including background image data, color palette data, text data, or logo image data. The method includes generate back ground image assets, text assets, and unique profile data based on the extracted signals. The method includes transmitting the media assets to a content item generation pipeline to generate a number of new candidate content items.

Abstract: An image generation method is performed by one or more data processing apparatus, and comprises: obtaining an image showing an object; generating one or more additional images related to the object; fine-tuning a machine-learned text-to-image model using one or more of the additional images; providing, to the machine-learned text-to-image model, a prompt to generate an output image showing the object, and obtaining, from the machine-learned text-to-image generation model, the output image.

Abstract: Techniques for determining an audience gap for a communication campaign presented herein. The system can obtain data indicating a content item for a communication campaign of a client account. The system can determine, based on the attribute, that the content item is associated with a first group type. The first group type can include a plurality of audience segments. Additionally, the system can determine, using performance data of the communication campaign of the client account, a first performance value of a first audience segment in the plurality of audience segments. Moreover, the system can determine, based on the first performance value transcending a performance threshold value, that the content item has an audience gap associated with the first audience segment. Furthermore, the system can perform an action based on the determination that the content item has the audience gap associated with the first audience segment.

Abstract: Methods, computing systems, and technology for automatically generating media assets and content items are presented. The system can receive data indicating a request for a plurality of media assets that comprise multiple media modalities. Additionally, the system can obtain a media asset profile for a client account associated with the request. Moreover, the system can generate, using a machine-learned media asset generation pipeline, the plurality of media assets based on the media asset profile by instructing a machine-learned asset generation model to generate media assets that align with the media asset preferences. Furthermore, the system can send, based on receiving data indicating selection of one or more of the plurality of media assets, the one or more of the plurality of media assets to a content item generation system for generating content items using the one or more of the plurality of media assets.