Abstract: A face-processing system is described for producing a target image based on a source image and driving information. The source image includes data depicting at least a face of a source subject having a source identity, a source pose, and a source expression. The driving information specifies one or more driving characteristics. The target image combines characteristics of the source image and the driving information. According to illustrative implementations, the face-processing system produces the target image by using plural warping subcomponents that operate at plural respective levels of a neural network and at increasing respective resolutions. Each warping subcomponent operates, in part, based on geometric displacement field (GDF) information that describes differences between a source mesh derived from the source image and a driving mesh derived from the driving information.

Abstract: Technologies disclosed herein relate to construction of a composite image to provide a parallax effect. An image is generated, and a first computer-readable image and a second computer-readable image are generated based upon the image. The first computer-readable image represents foreground of a scene and the second computer-readable image represents background of a scene. A location of eyes of a viewer relative to a display is determined, and the first computer-readable image is overlaid upon the second computer-readable image at a position that is based upon the location of the eyes of the viewer.

Abstract: A face-processing system is described for producing a target image based on a source image and driving information. The source image includes data depicting at least a face of a source subject having a source identity, a source pose, and a source expression. The driving information specifies one or more driving characteristics. The target image combines characteristics of the source image and the driving information. According to illustrative implementations, the face-processing system produces the target image by using plural warping subcomponents that operate at plural respective levels of a neural network and at increasing respective resolutions. Each warping subcomponent operates, in part, based on geometric displacement field (GDF) information that describes differences between a source mesh derived from the source image and a driving mesh derived from the driving information.

Abstract: A technique is described herein for temporally and spatially interpolating input video information, to produce output video information having a higher frame rate and a higher resolution compared to that exhibited by the input video information. The technique generates feature information based on plural frames of the input video information. The technique then produces the output video information based on the feature information using an architecture having, in order, a multi-stage encoding operation, a query-generating operation, and a multi-stage decoding operation. Each encoding stage produces an instance of encoder attention information that expresses identified relations across the plural frames of the input video information. Each decoding stage operates on an instance of encoder attention information produced by a corresponding encoding stage. The transformer architecture is compact and is capable of interpolating the input video information in real time.

Abstract: A computational photography system is described herein including a guidance system and a detail enhancement system. The guidance system uses a first neural network that maps an original image provided by an image sensor to a guidance image, which represents a color-corrected and lighting-corrected version of the original image. A combination unit combines the original image and the guidance image to produce a combined image. A detail-enhancement system then uses a second neural network to map the combined image to a predicted image. The predicted image supplements the guidance provided by the first neural network by sharpening details in the original image. A training system is also described herein for training the first and second neural networks. The training system alternates in the data it feeds the second neural network, first using a guidance image as input to the second neural network, and then using a corresponding ground-truth image.

Abstract: Systems and methods for generating interpolated images are disclosed. In examples, image features are extracted from a first image and a second image; such image features may be warped using first and second plurality of parameters. A first candidate intermediate frame may be generated based on the warped first features and the warped second features. Multi-scale features associated with the image features extracted from the first image and the second image may be obtained and warped using the first and second plurality of parameters. A second candidate intermediate frame may be generated based on the warped first multi-scale features and the warped second multi-scale features. By blending the first candidate intermedia frame with the second candidate intermediate frame, an interpolated image may be generated.

Abstract: A computational photography system is described herein including a guidance system and a detail enhancement system. The guidance system uses a first neural network that maps an original image provided by an image sensor to a guidance image, which represents a color-corrected and lighting-corrected version of the original image. A combination unit combines the original image and the guidance image to produce a combined image. A detail-enhancement system then uses a second neural network to map the combined image to a predicted image. The predicted image supplements the guidance provided by the first neural network by sharpening details in the original image. A training system is also described herein for training the first and second neural networks. The training system alternates in the data it feeds the second neural network, first using a guidance image as input to the second neural network, and then using a corresponding ground-truth image.

Abstract: Systems and methods for identifying contours of objects depicted in imagery are provided. A contour of an occluded object can be reconstructed based on a source contour extracted from an image and/or other geographic data. The source contour can be analyzed to identify a main contour direction for one or more points on the source contour. A plurality of rays can be extended from each of the one or more points based on the main contour direction associated with the point. A graph model can be constructed from the plurality of rays extended from the plurality of points. A path can be determined through the graph model and the contour can be constructed based at least in part on the determined path.

Abstract: Systems and methods for identifying contours of objects depicted in imagery are provided. A contour of an occluded object can be reconstructed based on a source contour extracted from an image and/or other geographic data. The source contour can be analyzed to identify a main contour direction for one or more points on the source contour. A plurality of rays can be extended from each of the one or more points based on the main contour direction associated with the point. A graph model can be constructed from the plurality of rays extended from the plurality of points. A path can be determined through the graph model and the contour can be constructed based at least in part on the determined path.