According to a general aspect, a method can include receiving a photo of a virtual conference participant, and a depth map based on the photo, and generating a plurality of synthesized images based on the photo. The plurality of synthesized images can have respective simulated gaze directions of the virtual conference participant. The method can also include receiving, during a virtual conference, an indication of a current gaze direction of the virtual conference participant. The method can further include animating, in a display of the virtual conference, an avatar corresponding with the virtual conference participant. The avatar can be based on the photo. Animating the avatar can be based on the photo, the depth map and at least one synthesized image of the plurality of synthesized images, the at least one synthesized image corresponding with the current gaze direction.

Systems and methods are directed to loading a view of the multiview image into memory. The view may be formatted as a bitmap defined by a pixel coordinate system. A distance between the view and a center point of view may be identified. Thereafter, the view may be rendered in a graphics pipeline as a sheared view according a shear function applied along an axis of the pixel coordinate system. A shear strength of the shear function correlates with the distance between the view and the center point of view.

Systems and methods for detecting information displayed by a display device and reflected into a camera feed are disclosed. Described systems and methods remove the reflected information from the camera feed so that unintended disclosure of private information when the camera feed is shared can be prevented.

Methods and apparatus are disclosed for implementing data augmentation within a storage controller of a data storage device based on machine learning data read from a non-volatile memory (NVM) array of a memory die. Some particular aspects relate to configuring the storage controller to generate augmented versions of training images for use in training a Deep Learning Accelerator of an image recognition system by rotating, translating, skewing, cropping, etc., a set of initial training images obtained from a host device and stored in the NVM array. Other aspects relate to controlling components of the memory die to generate noise-augmented images by, for example, storing and then reading training images from worn regions of the NVM array to inject noise into the images. Data augmentation based on data read from multiple memory dies is also described, such as image data spread across multiple NVM arrays or multiple memory dies.

Systems and methods for detecting information displayed by a display device and reflected into a camera feed are disclosed. Described systems and methods remove the reflected information from the camera feed so that unintended disclosure of private information when the camera feed is shared can be prevented.

A material data collection system allows capturing of material data. For example, the material data collection system may include digital image data for materials. The material data collection system may ensure that captured digital image data is properly aligned, so that material data may be easily recalled for later use, while maintaining the proper alignment for the captured digital image. The material data collection system may include using a capture guide, to provide cues on how to orient a mobile device used with the material data collection system.

Object overlay for vehicle occupants includes determining that visibility of an object in a view of a vehicle occupant through a transparent surface of the vehicle is degraded, the object being in an environment in which the vehicle travels, selecting from an image repository a reference image of the object, extracting, from the reference image, an image portion comprising an image of a least a portion of the object, transforming the extracted image portion to correspond to the view of the vehicle occupant, the transforming producing a transformed image portion, and displaying the transformed image portion on the transparent surface and interposed in a line-of-sight of the vehicle occupant to the object in the environment such that the transformed image portion overlays at least a portion of the vehicle occupant's view through the transparent surface to the object in the environment.

A material data collection system allows capturing of material data. For example, the material data collection system may include digital image data for materials. The material data collection system may ensure that captured digital image data is properly aligned, so that material data may be easily recalled for later use, while maintaining the proper alignment for the captured digital image. The material data collection system may include using a capture guide, to provide cues on how to orient a mobile device used with the material data collection system.

A computer-implemented method of determining relative velocity between a vehicle and an object. The method includes receiving sensor data generated by one or more sensors of the vehicle. The one or more sensors are configured to sense an environment through which the vehicle is moving by following a scan pattern comprising component scan lines. The method includes obtaining, by one or more processors, a point cloud frame based on the sensor data and representative of the environment and identifying, by the one or more processors, a point cloud object within the point cloud frame. The method further includes determining, by the one or more processors, that the point cloud object is skewed relative to an expected configuration of the point cloud object, and determining, by the one or more processors, a relative velocity of the point cloud object by analyzing the skew of the object.

Disclosed are a car damage picture angle correction method, an electronic device, and a readable storage medium. The method includes: after receiving a car damage picture to be classified and identified, identifying a rotation category corresponding to the received car damage picture by using a pre-trained picture rotation category identification model; determining a rotation control parameter corresponding to the identified rotation category according to a pre-determined mapping relation between rotation categories and rotation control parameters, the rotation control parameter including a rotation angle and a rotation direction; and rotating the received car damage picture according to the determined rotation control parameter, so as to generate an angle-normal car damage picture. The disclosure can perform car damage picture angle correction more comprehensively and more effectively with no need to artificially perform angle identification on a car damage picture and to manually rotate the picture, thereby achieving a higher efficiency and accuracy.