Abstract: A computer-implemented method is disclosed. The method includes selecting one or more target surfaces portrayed in at least one video frame, generating a video data latent space representation of the at least one video frame, accessing a plurality of supplemental data latent space representations of a plurality of supplemental data sets, identifying a particular supplemental data latent space representation based at least in part on the video data latent space representation, selecting a particular supplemental data set in response to identifying the particular supplemental data latent space representation, the particular supplemental data set corresponding with the particular supplemental data latent space representation, and inserting the particular supplemental data set into the at least one video frame.

Abstract: Systems, devices, and methods are provided for training and/or inferencing using machine-learning models. In at least one embodiment, a user selects a source media (e.g., video or audio file) and a target identity. A content embedding may be extracted from the source media, and an identity embedding may be obtained for the target identity. The content embedding of the source media and the identity embedding of the target identity may be provided to a transfer model that generates synthesized media. For example, a user may select a song that is sung by a first artist and then select a second artist as the target identity to produce a cover of the song in the voice of the second artist.

Abstract: An electronic device is disclosed. The electronic device includes a first housing, a second housing, a screen assembly, a shaft assembly, and a transmission member. The first housing and the second housing are in rotational fit with each other through the shaft assembly. The transmission member sleeves on the shaft assembly. The transmission member is capable of moving in an axial direction of the shaft assembly. The first housing is provided with a first pushing portion. The transmission member is provided with a first guide surface. The first pushing portion is abutted on the first guide surface. A first section and a second section of the screen assembly are disposed in the first housing and the second housing, respectively.

Abstract: The present disclosure provides methods for secure communication. In an example method, an optical line terminal (OLT) sends a first message to an optical network unit (ONU), where the first message includes a first key algorithm, a certificate of the OLT, and a public key of the OLT, and the first key algorithm is a key algorithm supported by both the OLT and the ONU. The ONU verifies the certificate of the OLT, and after the verification succeeds, the ONU determines a shared key based on the first key algorithm and the public key of the OLT. The ONU sends a second message to the OLT, where the second message includes a certificate of the ONU and a public key of the ONU. The OLT verifies the certificate of the ONU. After the verification succeeds, the OLT determines the shared key based on the first key algorithm and the public key of the ONU.

Abstract: Audio content and played frames may be received. The audio content may correspond to first video content. The played frames may be included in the first video content. The first video content may further include a replaced frame. The played frames and the replaced frame may include a face of a person. Location data may also be received that indicates locations of facial features of the face of the person within the replaced frame. A replacement frame may be generated, such as by rendering the facial features in the replacement frame based at least in part on the locations indicated by the location data and positions indicated by a portion of the audio content that is associated with the replaced frame. Second video content may be played including the played frames and the replacement frame. The replacement frame may replace the replaced frame in the second video content.

Abstract: A method of identifying defects in a fabric can include obtaining an image of a fabric on a loom, extracting feature points within the image to generate an input image, processing the input image with a machine learning model, detecting one or more defects within the input image using the machine learning model, and providing, by the machine learning model, an indication of a defect in the input image.