Abstract: Systems and techniques are provided for performing user authentication. For example, a process can include obtaining a plurality of images associated with a face and a facial expression of the user, wherein each respective image of the plurality of images includes a different portion of the face. An encoder neural network can be used to generate one or more predicted three-dimensional (3D) facial modeling parameters, wherein the encoder neural network generates the one or more predicted 3D facial modeling parameters based on the plurality of images. A reference 3D facial model associated with the face and the facial expression can be obtained. An error can be determined between the one or more predicted 3D facial modeling parameters and the reference 3D facial model, and the user can be authenticated user based on the error being less than a pre-determined authentication threshold.

Abstract: Fin smoothing, and integrated circuit structures resulting therefrom, are described. For example, an integrated circuit structure includes a semiconductor fin having a protruding fin portion above an isolation structure, the protruding fin portion having substantially vertical sidewalls. The semiconductor fin further includes a sub-fin portion within an opening in the isolation structure, the sub-fin portion having a different semiconductor material than the protruding fin portion. The sub-fin portion has a width greater than or less than a width of the protruding portion where the sub-fin portion meets the protruding portion. A gate stack is over and conformal with the protruding fin portion of the semiconductor fin. A first source or drain region at a first side of the gate stack, and a second source or drain region at a second side of the gate stack opposite the first side of the gate stack.

Abstract: Systems and techniques are provided for performing user authentication. For example, a process can include obtaining a plurality of images associated with a face and a facial expression of the user, wherein each respective image of the plurality of images includes a different portion of the face. An encoder neural network can be used to generate one or more predicted three-dimensional (3D) facial modeling parameters, wherein the encoder neural network generates the one or more predicted 3D facial modeling parameters based on the plurality of images. A reference 3D facial model associated with the face and the facial expression can be obtained. An error can be determined between the one or more predicted 3D facial modeling parameters and the reference 3D facial model, and the user can be authenticated user based on the error being less than a pre-determined authentication threshold.

Abstract: Systems and techniques are provided for generating a texture for a three-dimensional (3D) facial model. For example, a process can include obtaining a first frame, the first frame including a first portion of a face. In some aspects, the process can include generating a 3D facial model based on the first frame and generating a first facial feature corresponding to the first portion of the face. In some examples, the process includes obtaining a second frame, the second frame including a second portion of the face. In some cases, the second portion of the face at least partially overlaps the first portion of the face. In some examples, the process includes combining the first facial feature with the second facial feature to generate an enhanced facial feature, wherein the combining is performed to enhance an appearance of select areas of the enhanced facial feature.

Abstract: Systems and techniques are provided for generating a three-dimensional (3D) facial model. For example, a process can include obtaining at least one input image associated with a face. In some aspects, the process can include obtaining a pose for a 3D facial model associated with the face. In some examples, the process can include generating, by a machine learning model, the 3D facial model associated with the face. In some cases, one or more parameters associated with a shape component of the 3D facial model are conditioned on the pose. In some implementations, the 3D facial model is configured to vary in shape based on the pose for the 3D facial model associated with the face.

Abstract: Imaging systems and techniques are described. An imaging system receives, from an image sensor, image(s) of a user (e.g., in a pose and/or with a facial expression). The image sensor captures the first set of image(s) in a first electromagnetic (EM) frequency domain, such as the infrared and/or near-infrared domain. The imaging system generates a representation of the user in the first pose in a second EM frequency domain (e.g., visible light domain) at least in part by inputting the image(s) into one or more trained machine learning models. The representation of the user is based on an image property associated with image data of at least the part of the user in the second EM frequency domain. The imaging system outputs the representation of the user in the pose in the second EM frequency domain.