Abstract: In various embodiments, a method for processing video streams is described. A plurality of video streams for transmission to a display device are received. The plurality of video streams have respective initial image quality levels. An estimated gaze location of a user of the display device is estimated. At least one video stream of the plurality of video streams is processed to have a modified image quality level based on the estimated gaze location. The modified image quality level is less than a corresponding initial image quality level. The plurality of video streams are transmitted to the display device.

Abstract: In various embodiments, a computer-implemented method of training a neural network for relighting an image is described. A first training set that includes source images and a target illumination embedding is generated, the source images having respective illuminated subjects. A second training set that includes augmented images and the target illumination embedding is generated, where the augmented images corresponding to the source images. A first autoencoder is trained using the first training set to generate a first output set that includes estimated source illumination embeddings and first reconstructed images that correspond to the source images, the reconstructed images having respective subjects that are i) from the corresponding source image, and ii) illuminated based on the target illumination embedding.

Abstract: Aspects of the present disclosure relate to adjusting an illumination of a user depicted in one or more images when using a video conferencing application. In one example, one or more images depicting the user may be received from an image sensor. Further, an illumination of the user depicted in the one or more images may be determined to be unsatisfactory. For example, the user's face may be too bright or too dim. Accordingly, content displayed at a display device may identified and then modified. The modified content may then be rendered to a display device thereby changing the illumination of the user depicted in subsequent images. In examples, the modified content may include a graphical element, such as a ring of a specific color at least partially surrounding content rendered to and displayed at the display device.

Abstract: Aspects of the present disclosure relate to systems and methods for controlling a function of a computing system using gaze detection. In examples, one or more images of a user are received and gaze information may be determined from the received one or more images. Non-gaze information may be received when the gaze information is determined to satisfy a condition. Accordingly, a function may be enabled based on the received non-gaze information. In examples, the gaze information may be determined by extracting a plurality of features from the received one or more images, providing the plurality of features to a neural network, and determining, utilizing the neural network, a location at a display device at which a gaze of the user is directed.

Abstract: Technology is described herein that uses an object-encoding system to convert an object image into a combined encoding. The object image depicts a reference object, while the combined encoding represents an environment image. The environment image, in turn, depicts an estimate of an environment that has produced the illumination effects exhibited by the reference object. The combined encoding includes: a first part that represents image content in the environment image within a high range of intensities values; and a second part that represents image content within a low range of intensity values. Also described herein is a training system that trains the object-encoding system based on combined encodings produced by a separately-trained environment-encoding system. Also described herein are various applications of the object-encoding system and environment-encoding system.

Abstract: Technology is described herein that uses an object-encoding system to convert an object image into a combined encoding. The object image depicts a reference object, while the combined encoding represents an environment image. The environment image, in turn, depicts an estimate of an environment that has produced the illumination effects exhibited by the reference object. The combined encoding includes: a first part that represents image content in the environment image within a high range of intensities values; and a second part that represents image content within a low range of intensity values. Also described herein is a training system that trains the object-encoding system based on combined encodings produced by a separately-trained environment-encoding system. Also described herein are various applications of the object-encoding system and environment-encoding system.

Abstract: A computing system includes an encoder that receives an input image and encodes the input image into real image features, a decoder that decodes the real image features into a reconstructed image, a generator that receives first audio data corresponding to the input image and generates first synthetic image features from the first audio data, and receives second audio data and generates second synthetic image features from the second audio data, a discriminator that receives both the real and synthetic image features and determines whether a target feature is real or synthetic, and a classifier that classifies a scene of the second audio data based on the second synthetic image features.

Abstract: A computing system includes an encoder that receives an input image and encodes the input image into real image features, a decoder that decodes the real image features into a reconstructed image, a generator that receives first audio data corresponding to the input image and generates first synthetic image features from the first audio data, and receives second audio data and generates second synthetic image features from the second audio data, a discriminator that receives both the real and synthetic image features and determines whether a target feature is real or synthetic, and a classifier that classifies a scene of the second audio data based on the second synthetic image features.

Abstract: A computing system includes an encoder that receives an input image and encodes the input image into real image features, a decoder that decodes the real image features into a reconstructed image, a generator that receives first audio data corresponding to the input image and generates first synthetic image features from the first audio data, and receives second audio data and generates second synthetic image features from the second audio data, a discriminator that receives both the real and synthetic image features and determines whether a target feature is real or synthetic, and a classifier that classifies a scene of the second audio data based on the second synthetic image features.

Abstract: A method for image enhancement on a computing device includes receiving a digital input image depicting a human eye. From the digital input image, the computing device generates a gaze-adjusted image via a gaze adjustment machine learning model by changing an apparent gaze direction of the human eye. From the gaze-adjusted image and potentially in conjunction with the digital input image, the computing device generates a detail-enhanced image via a detail enhancement machine learning model by adding or modifying details. The computing device outputs the detail-enhanced image.