Lashify, Inc.

A kit includes artificial lash extensions. Each of the artificial lash extensions includes groupings of artificial hairs and a knotless base. The groupings protrude from the knotless base along a length of the base. The knotless bases of the artificial lash extensions attach to an underside of natural eyelashes in an arrangement adjacent to one another. The kit also includes an applicator having a pair of opposing arms that are joined to each other at a proximal end of each arm. Each arm includes a first section having the proximal end of the arm and a second section connected to the first section of the arm. The second section includes a first end portion, a second end portion, and a curvature disposed between the first end portion and the second end portion.

A method for training a machine learning model using information pertaining to an eye area of a human face, the method includes generating training data for the machine learning model. Generating the training data includes generating a first training input, the first training input including information representing 2D images of human faces, and generating a first target output for the training input. The first target output identifies, for each of the 2D images of human faces, information identifying an eye shape category of multiple eye shape categories, the eye shape category corresponding to a shape of an eye represented in the respective 2D image. The method further includes providing the training data to train the machine learning model on (i) a set of training inputs including the first training input, and (ii) a set of target outputs including the first target output.

A first computer vision operation is performed on a first video segment of a video stream to track points corresponding to an underside of natural eyelashes of an eye area of a subject. The first video segment is modified to include a first overlay of a first model of a first artificial lash extension positioned at least at a first point of the points corresponding to the underside of the natural eyelashes. The first modified video segment is provided for presentation at a client device associated with the subject.

A method includes receiving, by a processing device and from a client device, a first video segment of a video stream. The method further includes generating, using the first video segment, a three-dimensional (3D) model of a human face of a subject. The method further includes modifying the first video segment to include i) a first overlay of the 3D model identifying one or more landmarks and ii) first visual indicia positioned in the first video segment based on the one or more landmarks of the 3D model and related to a first operation of a plurality of operations of an application of artificial lash extensions. The method further includes providing the first modified video segment for presentation at the client device, the first modified video segment including the first visual indicia related to the first operation of the application of artificial lash extensions.

Two-dimensional ( 2 D) image data corresponding to a 2 D image of an eye area of a subject is received. Three-dimensional ( 3 D) data representing the eye area of the subject is identified based on the 2 D image data. A first landmark representing a first facial feature of the eye area, a second landmark representing a second facial feature of the eye area, and a relationship between the first landmark and the second landmark is determined based on the 3 D data representing the eye area of the subject. A subset of the plurality of false eyelashes comprising a subset of a plurality of artificial lash extensions designed for application at an underside of natural eyelashes of the subject are identified, among a plurality of false eyelashes, based on the relationship between the first landmark and the second landmark.

A method for training a machine learning model using information pertaining to an eye area of a human face, the method includes generating training data for the machine learning model. Generating the training data includes generating a first training input, the first training input including information representing 2D images of human faces, and generating a first target output for the training input. The first target output identifies, for each of the 2D images of human faces, information identifying an eye shape category of multiple eye shape categories, the eye shape category corresponding to a shape of an eye represented in the respective 2D image. The method further includes providing the training data to train the machine learning model on (i) a set of training inputs including the first training input, and (ii) a set of target outputs including the first target output.

A method includes determining a textual identifier that describes at least one facial feature of a human face based on two-dimensional image data representing the human face. The method further includes generating a prompt for a generative machine learning model. The prompt includes information corresponding to the textual identifier that describes the at least one facial feature of the human face. The method further includes obtaining, from the generative machine learning model and based on the prompt, an output indicative of a set of false eyelashes that suit the human face.

A first computer vision operation is performed on a first video segment of a video stream to track points corresponding to an underside of natural eyelashes of an eye area of a subject. The first video segment is modified to include a first overlay of a first model of a first artificial lash extension positioned at least at a first point of the points corresponding to the underside of the natural eyelashes. The first modified video segment is provided for presentation at a client device associated with the subject.

A method includes receiving, by a processing device and from a client device, a first video segment of a video stream. The method further includes generating, using the first video segment, a three-dimensional (3D) model of a human face of a subject. The method further includes modifying the first video segment to include i) a first overlay of the 3D model identifying one or more landmarks and ii) first visual indicia positioned in the first video segment based on the one or more landmarks of the 3D model and related to a first operation of a plurality of operations of an application of artificial lash extensions. The method further includes providing the first modified video segment for presentation at the client device, the first modified video segment including the first visual indicia related to the first operation of the application of artificial lash extensions.