Abstract: Techniques for generating a gallery view of tiles for in-area participants who are participating in an online meeting are disclosed. A video stream is accessed, where this stream includes an area view of an area in which an in-area participant is located. This area view comprises pixels representative of the area and pixels representative of the in-area participant. The pixels representative of the in-area participant are identified. A field of view of the in-area participant is generated. A tile of the in-area participant is generated based on the field of view. This tile is then displayed while the area view is not displayed.

Abstract: Systems and methods are provided for configuring and utilizing botcasts, which comprise audio content with transitions corresponding to the audio content, to facilitate accessibility and presentation of the media content within the botcasts according to contextual relevance for different individual users. The systems identify, access, filter, augment, customize, personalize, create and/or otherwise configure the media content, as well as the content transitions in the botcasts, according to the individual preferences and profiles of each user, as well as the contextual circumstances for each user.

Abstract: The disclosed technology is generally directed to audio capture. In one example of the technology, recorded sounds are received such that the sounds recorded were emitted from multiple locations in an environment and such that the sounds recorded are sounds that can be converted to room impulse responses. The room impulse responses are generated from the recorded sounds. Location information that is associated with the multiple locations is received. At least the room impulses responses and the location information are used to generate at least one environment-specific model. Audio captured in the environment is received. An output is generated by processing the captured audio with the at least one environment-specific model such that the output includes at least one adjustment of the captured audio based on at least one acoustical property of the environment.

Abstract: In order for the feature extractors to operate with sufficient accuracy, a high degree of training is required. In this situation, a neural network implementing the feature extractor may be trained by providing it with images having known correspondence. A 3D model of a city may be utilized in order to train a neural network for location detection. 3D models are sophisticated and allow manipulation of viewer perspective and ambient features such as day/night sky variations, weather variations, and occlusion placement. Various manipulations may be executed in order to generate vast numbers of image pairs having known correspondence despite having variations. These image pairs with known correspondence may be utilized to train the neural network to be able to generate feature maps from query images and identify correspondence between query image feature maps and reference feature maps. This training can be accomplished without requiring the capture of real images with known correspondence.

Abstract: In order for the feature extractors to operate with sufficient accuracy, a high degree of training is required. In this situation, a neural network implementing the feature extractor may be trained by providing it with images having known correspondence. A 3D model of a city may be utilized in order to train a neural network for location detection. 3D models are sophisticated and allow manipulation of viewer perspective and ambient features such as day/night sky variations, weather variations, and occlusion placement. Various manipulations may be executed in order to generate vast numbers of image pairs having known correspondence despite having variations. These image pairs with known correspondence may be utilized to train the neural network to be able to generate feature maps from query images and identify correspondence between query image feature maps and reference feature maps. This training can be accomplished without requiring the capture of real images with known correspondence.

Abstract: An image abstraction engine is provided to characterize scenes like typically found in an urban setting. Specifically buildings and manmade structures have certain characteristic properties that may be abstracted and compressed in a manner that takes advantage of those characteristic properties. This allows for a more compact and computationally efficient abstraction and recognition.

Abstract: An image abstraction engine is provided to characterize scenes like typically found in an urban setting. Specifically buildings and manmade structures have certain characteristic properties that may be abstracted and compressed in a manner that takes advantage of those characteristic properties. This allows for a more compact and computationally efficient abstraction and recognition.