Provided are systems and methods for face reenactment. An example method includes receiving a target video that includes at least one target frame, where the at least one target frame includes a target face, receiving a scenario including a series of source facial expressions, determining, based on the target face, a target facial expression of the target face, synthesizing, based on a parametric face model and a texture model, an output face including the target face, where the target facial expression of the target face is modified to imitate a source facial expression of the series of source facial expressions, and generating, based on the output face, a frame of an output video. The parametric face model includes a template mesh pre-generated based on historical images of faces of a plurality of individuals, where the template mesh includes a pre-determined number of vertices.

A system includes a network interface, a processing system, and a memory system. The memory system stores instructions that when executed by the processing system result in receiving a request and request data associated with a user from a web server and analyzing the request data to identify a primary offer associated with the request. A look-alike model is accessed to determine at least one secondary offer based on one or more of: the request, the request data, and the primary offer. The primary offer and the at least one secondary offer are provided for presentation to the user through a user interface.

Digital image selection techniques are described that employ machine learning to select a digital image of an object from a plurality of digital images of the object. The plurality of digital images each capture the object for inclusion as part of generating digital content, e.g., a webpage, a thumbnail to represent a digital video, and so on. In one example, digital image selection techniques are described that employ machine learning to select a digital image of an object from a plurality of digital images of the object. As a result, the service provider system may select a digital image of an object from a plurality of digital images of the object that has an increased likelihood of achieving a desired outcome and may address the multitude of different ways in which an object may be presented to a user.

In a computer apparatus, at least processor is arranged to determine values for two or more candidate advertisements. Based on the determined values, one of the candidate advertisements is selected. The selected advertisement is displayed on a user interface.

Apparatus and methods present a content item and question from an inquirer to a group of users in a first feedback session with a requirement for the users to provide predictions of how a member of a distinct target group will respond to the content item, receiving, during the first feedback session, the first group's prediction of the target group's responses, presenting, during a second feedback session, the same content item and the same question to the target group with a requirement for the target group to provide responses directed to their own responses, constructing a predictive model of the target group based on responses received from the target group during the second feedback session, measuring accuracy of the first group's predictions using the target group predictive model and providing the inquirer access to an ordered visual representation of the first group users arranged as a function of accuracy.

The present disclosure provides a computer system (112). The computer system (112) performs a method for encoding user count with a low memory footprint. The method includes a first step of receiving real-time and adaptive frequency of user visibility. Further, the method includes another step of receiving a user device (106) id associated with one or more users (104). Furthermore, the method includes yet another step of encoding the user visibility count. The frequency of the user visibility is the number of times the computer system receives a request from a user device (106). The user device (106) id is a unique string of numbers and letters. The unique string of numbers and letters identifies the user device (106) associated with one or more user (104). The user visibility count is encoded by using one or more data structures and one or more algorithms.

Systems, methods, and computer-readable media are disclosed for estimating impressions for a digital out of home (DOOH) advertising spaces (e.g., digital billboards and screens). A DOOH advertising system may determine the location of relevant DOOH advertising spaces and the location of certain consumers with known attributes and a known location. Based on this information the DOOH advertising system may estimate a number of impressions for a given DOOH advertising space and a given consumer segment associated with attributes of consumers within a certain distance from the DOOH advertising space. Using this information, the DOOH advertising spaces having the highest estimated impressions for a given consumer segment may be identified.

A method, which comprises determining a logical identifier used for communication over a network portion managed by a service provider and transmitting to a computing apparatus adapted to effect online transactions involving a payer and a payee an indication of the service provider having agreed to act as the payer for at least one online transaction requested by a requesting device that uses the logical identifier. Also, a method, which comprises determining a logical identifier used to identify a device during a request for an online transaction; identifying, based on the logical identifier, a third party that has agreed to act as a payer for the online transaction; providing a user of the device with an opportunity to confirm the third party as the payer for the online transaction; and completing the online transaction based on input from the user.

The present disclosure presents systems and related methods for creating real-time predictions. One such method comprises receiving, by a computing device, a first set of data and a second set of data, wherein the first set of data comprises a plurality of items available from a first source for a first set of users and the second set of data comprises transaction purchase data for a second set of users that have reward accounts, utilizing a predictive data model that determines a propensity score for a user from only behavior data that is not attributed to the user; receiving a third set of data from a third source comprising social media channel data for a third set of users; and updating the predictive data model to determine the propensity score for the user based at least in part on the third set of data.

Methods and systems for determining an individual gaze value are disclosed herein. An exemplary method involves: (a) receiving gaze data for a first wearable computing device, wherein the gaze data is indicative of a wearer-view associated with the first wearable computing device, and wherein the first wearable computing device is associated with a first user-account; (b) analyzing the gaze data from the first wearable computing device to detect one or more occurrences of one or more advertisement spaces in the gaze data; (c) based at least in part on the one or more detected advertisement-space occurrences, determining an individual gaze value for the first user-account; and (d) sending a gaze-value indication, wherein the gaze-value indication indicates the individual gaze value for the first user-account.