Abstract: Methods, systems, and apparatuses to fuse a first dataset with a second dataset, and determine head pose estimation(s) based on the fused first dataset and second dataset. The first dataset may be associated with sensor data generated by a set of sensors of a first device, while the second dataset may be associated with sensor data generated by a first sensor of an apparatus. For example, an apparatus may obtain the first dataset and the second dataset. Additionally, the apparatus may generate a fused dataset based on the first dataset and the second dataset, and determine a head pose estimation of a head of the user based on the fused dataset. Further, the apparatus may output the third head pose estimation.

Abstract: The invention relates to the field of aluminum wheel casting molds, and more particularly relates to a closed-loop control method and system for a mold temperature in a wheel casting process. The control method includes: step 1, acquiring data, that is, acquiring a plurality of mold position temperatures, and cooling pipeline opening and closing signals in a target wheel casting process according to a fixed frequency; step 2, storing, based on acquired mold opening and closing signals of casting equipment, the acquired data in a database in the form of a unique ID according to a single wheel casting process; step 3, calculating new process parameters based on the acquired plurality of position temperatures and time; and step 4, integrating the calculated process parameters, and issuing the process parameters to a PLC of a casting equipment to perform new casting.

Abstract: A multimedia playback method includes: playing first multimedia data, switching from playing the first multimedia data to playing a multimedia frame corresponding to a target starting point of second multimedia data, based on a media switching operation, the second multimedia data including multimedia frames corresponding to a plurality of time points, the plurality of time points including pre-configured P candidate starting points, and the target starting point belonging to the P candidate starting points, and switching, during the playing of the second multimedia data, from playing a first multimedia frame in the second multimedia data to playing a new multimedia frame corresponding to a new starting point, based on a target triggering operation, the first multimedia frame being a multimedia frame played at a moment at which the target triggering operation is performed, and the new starting point being a candidate starting point other than the target starting point. .

Abstract: Systems and techniques are described for establishing one or more virtual sessions between users. For instance, a first device can transmit, to a second device, a call establishment request for a virtual representation call for a virtual session and can receive, from the second device, a call acceptance indicating acceptance of the call establishment request. The first device can transmit, to the second device, first mesh information for a first virtual representation of a first user of the first device and first mesh animation parameters for the first virtual representation. The first device can receive, from the second device, second mesh information for a second virtual representation of a second user of the second device and second mesh animation parameters for the second virtual representation. The first device can generate, based on the second mesh information and the second mesh animation parameters, the second virtual representation of the second user.

Abstract: Techniques and systems are provided for authenticating a user of a device. For example, input biometric data associated with a person can be obtained. A similarity score for the input biometric data can be determined by comparing the input biometric data to a set of templates that include reference biometric data associated with the user. The similarity score can be compared to an authentication threshold. The person is authenticated as the user when the similarity score is greater than the authentication threshold. The similarity score can also be compared to a learning threshold that is greater than the authentication threshold. A new template including features of the input biometric data is saved for the user when the similarity score is less than the learning threshold and greater than the 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: Techniques are provided for generating three-dimensional models of objects from one or more images or frames. For example, at least one frame of an object in a scene can be obtained. A portion of the object is positioned on a plane in the at least one frame. The plane can be detected in the at least one frame and, based on the detected plane, the object can be segmented from the plane in the at least one frame. A three-dimensional (3D) model of the object can be generated based on segmenting the object from the plane. A refined mesh can be generated for a portion of the 3D model corresponding to the portion of the object positioned on the plane.

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: Systems and techniques are provided for performing scene segmentation and object tracking. For example, a method for processing one or more frames. The method may include determining first one or more features from a first frame. The first frame includes a target object. The method may include obtaining a first mask associated with the first frame. The first mask includes an indication of the target object. The method may further include generating, based on the first mask and the first one or more features, a representation of a foreground and a background of the first frame. The method may include determining second one or more features from a second frame and determining, based on the representation of the foreground and the background of the first frame and the second one or more features, a location of the target object in the second frame.

Abstract: Examples are described of segmenting an image into image regions based on depicted categories of objects, and for refining the image regions semantically. For example, a system can determine that a first image region in an image depicts a first category of object. The system can generate a color distance map of the first image region that maps color distance values to each pixel in the first image region. A color distance value quantifies a difference between a color value of a pixel in the first image region and a color value of a sample pixel in a second image region in the image. The system can process the image based on a refined variant of the first image region that is refined based on the color distance map, for instance by removing pixels from the first image region whose color distances fall below a color distance threshold.

Abstract: Systems and techniques are provided for facial expression recognition. In some examples, a system receives an image frame corresponding to a face of a person. The system also determines, based on a three-dimensional model of the face, landmark feature information associated with landmark features of the face. The system then inputs, to at least one layer of a neural network trained for facial expression recognition, the image frame and the landmark feature information. The system further determines, using the neural network, a facial expression associated with the face.

Abstract: Techniques are provided for generating three-dimensional models of objects from one or more images or frames. For example, at least one frame of an object in a scene can be obtained. A portion of the object is positioned on a plane in the at least one frame. The plane can be detected in the at least one frame and, based on the detected plane, the object can be segmented from the plane in the at least one frame. A three-dimensional (3D) model of the object can be generated based on segmenting the object from the plane. A refined mesh can be generated for a portion of the 3D model corresponding to the portion of the object positioned on the plane.

Abstract: Disclosed are systems, apparatuses, processes, and computer-readable media to implement a heterogenous biometric authentication process in a control system. For example, a method may include detecting the presence of a first person at a first time period and in an area associated with a function controlled by a control system. The method may include transmitting an authentication request to a first device detected by the control system, and receiving an authentication response from the first device. The authentication response includes information related to a biometric authentication performed at the first device. The method may further include authenticating the first person in the control system based on the information related to the biometric authentication. The method may then perform the function based on the authentication.

Abstract: Systems and techniques are described for image processing. An imaging system receives an identity image and an attribute image. The identity image depicts a first person having an identity. The attribute image depicts a second person having an attribute, such as a facial feature, an accessory worn by the second person, and/or an expression. The imaging system uses trained machine learning model(s) to generate a combined image based on the identity image and the attribute image. The combined image depicts a virtual person having both the identity of the first person and the attribute of the second person. The imaging system outputs the combined image, for instance by displaying the combined image or sending the combined image to a receiving device. In some examples, the imaging system updates the trained machine learning model(s) based on the combined image.

Abstract: The present disclosure generally relates to systems and techniques for constructing three-dimensional (3D) models. Certain aspects of the present disclosure provide an apparatus for model generation. The apparatus generally includes a memory, and one or more processors coupled to the memory. The one or more processors and the memory may be configured to receive one or more images depicting an object to be modeled, determine a category associated with the object to be modeled, select a shape of a space based on the category, and generate a 3D model of the object at least in part by carving one or more points associated with the space based on the one or more images depicting the object.

Abstract: Methods, systems, and apparatuses are provided to automatically determine whether an image is spoofed. For example, a computing device may obtain an image, and may execute a trained convolutional neural network to ingest elements of the image. Further, and based on the ingested elements of the image, the executed trained convolutional neural network generates an output map that includes a plurality of intensity values. In some examples, the trained convolutional neural network includes a plurality of down sampling layers, a plurality of up sampling layers, and a plurality of joint spatial and channel attention layers. Further, the computing device may determine whether the image is spoofed based on the plurality of intensity values. The computing device may also generate output data based on the determination of whether the image is spoofed, and may store the output data within a data repository.

Abstract: Systems and techniques are provided for performing video-based activity recognition. For example, a process can include extracting, using a first machine learning model, first one or more features from a first frame and second one or more features from a second frame. The first one or more features and the second one or more features are associated with a person driving a vehicle. The process can include processing, using a second machine learning model, the first one or more features and the second one or more features. The process can include determining, based on processing of the first one or more features and the second one or more features using the second machine learning model, at least one activity associated with the person driving the vehicle.

Abstract: A method for picture processing is described. A first tracking area is obtained. A second tracking area is also obtained. The method includes beginning to track the first tracking area and the second tracking area. Picture processing is performed once a portion of the first tracking area overlapping the second tracking area passes a threshold.

Abstract: A method for picture processing is described. A first tracking area is obtained. A second tracking area is also obtained. The method includes beginning to track the first tracking area and the second tracking area. Picture processing is performed once a portion of the first tracking area overlapping the second tracking area passes a threshold.

Abstract: A method for interactive image caricaturing by an electronic device is described. The method includes detecting at least one feature location of an image. The method further includes generating, based on the at least one feature location, an image mesh that comprises a grid of at least one horizontal line and at least one vertical line. The method additionally includes obtaining a gesture input. The method also includes determining at least one caricature action based on the at least one gesture input. The method further includes generating a caricature image based on the image mesh, the at least one caricature action and the image.