The present disclosure relates to the field of artificial intelligence (AI) and neural rendering, and particularly to a method of generating a multi-layer representation of a scene using neural networks trained in an end-to-end fashion and to a computing device implementing the method. The method of generating a multi-layer representation of a scene includes: obtaining a pair of images of the scene, the pair of the images comprising a reference image and a source image; performing a reprojection operation on the pair of images to generate a plane-sweep volume; predicting, using a geometry network, a layered structure of the scene based on the plane-sweep volume; and estimating, using a coloring network, color values and opacity values for the predicted layered structure of the scene to obtain the multi-layer representation of the scene; wherein the geometry network and the coloring network are trained in end-to-end manner.

The present disclosure generally relates to a system and method for creating and sharing event visualizations. More specifically, the present disclosure relates to generating event visualizations that are fully contained expressions of future experiences, representing venue spaces, and including items and services, for example. Avatars of the attendees can be overlaid onto each event visualization to visualize the attending guests. Each event visualization can be shared across one or more social network platform and via SMS, e-mails, etc. for inviting guests to attend a private event while maintaining control which guests to include. Additionally, the event visualizations can be shown as annotations on a map filterable by geographic location and time.

One embodiment provides a graphics processor comprising a set of processing resources configured to perform a supersampling operation via a mixed precision convolutional neural network, the set of processing resources including circuitry configured to receive, at an input block of a neural network model, history data, velocity data, and current frame data, pre-process the history data, velocity data, and current frame data to generate pre-processed data, provide the pre-processed data to a feature extraction network of the neural network model, process the pre-processed data at the feature extraction network via one or more encoder stages and one or more decoder stages, and generate an output image via an output block of the neural network model via direct reconstruction or kernel prediction.

This application discloses an image rendering method in a panoramic application. In the method, a foreground image is first rendered, and then a panoramic image used as a background is rendered. A pixel corresponding to the foreground image has a corresponding depth value. When the panoramic image is rendered, content corresponding to the panoramic image may be rendered at a pixel corresponding to a depth standard value based on a depth value of a pixel on a canvas. The depth reference value is a depth value of a pixel other than the pixel corresponding to the foreground image. In this way, repeated rendering is avoided for an overlapping part between the foreground image and the panoramic image, which not only reduces resource wastes and rendering overheads, but also improves rendering efficiency.

The present disclosure illustrates systems and methods for automatically adjusting a following 3D asset based on a deformation of a related base 3D asset. The systems and methods may use geomaps to index the relationship between the following 3D asset and base 3D asset. By automatically adjusting a following 3D asset based on the base 3D asset, the following 3D asset may retain full functionality.

The present disclosure relates to methods, devices, and systems for blending geographic data when combining geographic data sources. The methods, devices, and systems identify a blend region for transitioning between a first dataset and a second dataset. The methods, devices, and systems extrapolate geographic data from the second dataset to blend with the geographic data from the first dataset to create blended elevation data in the blend region. The methods, devices, and systems may generate an image for a geographic region with the first set of geographic data, the second set of geographic data, and the blended elevation data.

The present disclosure relates to methods and apparatus for graphics processing. The present disclosure can calculate a center-edge distance of a first pixel, the center-edge distance of the first pixel equal to a distance from a first pixel center to one or more edges of a first primitive. Additionally, the present disclosure can store the center-edge distance of the first pixel when the first primitive is visible in a scene. The present disclosure can also determine an amount of overlap between the first pixel and the first primitive. Further, the present disclosure can blend a color of the first pixel with a color of a second pixel based on at least one of the center-edge distance of the first pixel or the amount of overlap between the first pixel and the first primitive.

A computer simulation object such as a chair is described by voice or photo input to render a 2D image. Machine learning may be used to convert voice input to the 2D image. The 2D image is converted to a 3D asset and the 3D asset or portions thereof are used in the computer simulation, such as a computer game, as the object such as a chair.

Systems, apparatuses and methods may provide away to render edges of an object defined by multiple tessellation triangles. More particularly, systems, apparatuses and methods may provide a way to perform anti-aliasing at the edges of the object based on a coarse pixel rate, where the coarse pixels may be based on a coarse Z value indicate a resolution or granularity of detail of the coarse pixel. The systems, apparatuses and methods may use a shader dispatch engine to dispatch raster rules to a pixel shader to direct the pixel shader to include, in a tile and/or tessellation triangle, one more finer coarse pixels based on a percent of coverage provided by a finer coarse pixel of a tessellation triangle at or along the edge of the object.

This technology relates to rendering content from discrete applications. In this regard, one or more computing devices may receive a global scene graph containing resources provided by two or more discrete processes, wherein the global scene graph is instantiated by a first process of the two or more discrete processes. The one or more computing devices may render and output for display, the global scene graph in accordance with the resources contained there.