Abstract: Techniques for providing blended physical and virtual reality experiences are disclosed. In some embodiments, a pose of a camera of a device located in a venue is determined based at least in part on three-dimensional asset recognition in a field of view of the camera, wherein the field of view of the camera comprises an actual physical view of the venue; a virtual view corresponding to the actual view is obtained from existing assets comprising a specification of a virtualized version of the venue; and display of the virtual view is facilitated at the venue. Thus, a virtual reality of a physical space is provided while at the physical space based on actual three-dimensional objects detected or recognized in the physical space.

Abstract: Improved techniques for generating video content are disclosed. In some embodiments, a frame is rendered using a first rendering option. It is then determined whether the frame is an independent frame (I-frame) or a predictive frame (P-frame). In the event that the frame is an I-frame, the frame is rendered using a second rendering option that has a higher computational complexity than the first rendering option and encoded independently. In the event that the frame is a P-frame, motion vectors and prediction error with respect to the frame and one or more other frames are determined and encoded.

Abstract: Techniques for generating an arbitrary view of a scene are disclosed. In some embodiments, arbitrary view generation includes storing sets of images associated with corresponding assets in a database and generating an image comprising a requested view of a scene using images associated with one or more assets comprising the scene that are stored in the database.

Abstract: Adaptive sampling of pixels is disclosed. In some embodiments, a rendering of a scene is generated by sampling each pixel of the rendering with a prescribed number of samples. Subsequently, those pixels of the rendering that do not satisfy a noise threshold are iteratively sampled with an additional sample during each iteration until all pixels of the rendering satisfy the noise threshold. The noise threshold is associated with noise arising due to under sampling. Pixels comprising the completed rendering are not uniformly sampled.

Abstract: Techniques for generating an arbitrary view of an asset are disclosed. In some embodiments, arbitrary view generation includes storing a set of images associated with an asset in a database and generating an image comprising a requested view of the asset using at least a subset of the set of images associated with the asset that are stored in the database.

Abstract: Techniques for generating three-dimensional content from the recordings of multiple independently operated cameras that are not constrained to fixed positions and orientations are disclosed. In some embodiments, data from a plurality of cameras configured to capture a scene is received; a relative pose of each camera with respect to the scene is determined based at least in part on a first estimate and a second estimate, wherein the first estimate is based on image data and the second estimate is based on sensor data; relative poses of cameras with respect to one or more other cameras are determined based on determined relative poses of individual cameras with respect to the scene; and a three-dimensional reconstruction of at least a portion of the scene is generated based on the received data and determined relative poses.

Abstract: Techniques for virtualizing content are disclosed. Elements comprising source content are virtualized by mapping each to and representing each with a corresponding database object. A specification of the corresponding database objects is provided for rendering the source content instead of any original pixel information of the source content so that a virtualized version of the source content is rendered.

Abstract: Techniques for delivering virtualized content are disclosed. In some embodiments, elements in source content are mapped to a set of corresponding database objects, and the set of database objects is modified to generate a virtualized version of the source content. A specification of the virtualized version of the source content is provided to an output device in response to a request from the output device for the source content.

Abstract: Techniques for generating an arbitrary view of an asset are disclosed. In some embodiments, arbitrary view generation includes storing a set of images of an asset, wherein each image comprising at least a subset of the set of images is rendered from a three-dimensional model of the asset, and generating an image comprising an arbitrary perspective of the asset at least in part by populating the image comprising the arbitrary perspective with pixels harvested from one or more images comprising the set of images.

Abstract: A machine learning based image processing architecture and associated applications are disclosed herein. In some embodiments, a machine learning framework is trained to learn low level image attributes such as object/scene types, geometries, placements, materials and textures, camera characteristics, lighting characteristics, contrast, noise statistics, etc. Thereafter, the machine learning framework may be employed to detect such attributes in other images and process the images at the attribute level.

Abstract: Techniques for delivering virtualized content are disclosed. In some embodiments, source content is virtualized by mapping elements in the source content to existing database objects, and a specification of the virtualized version of the source content is provided to an output device in response to a request from the output device for the source content. In some such cases, a format of the source content, a format of the virtualized version of the source content, and a native format in which the virtualized version of the source content is rendered at the output device all comprise different formats.

Abstract: Techniques for virtualizing content are disclosed. One or more objects comprising source video content are determined. The one or more objects comprising the source video content are virtualized by mapping each to and representing each with a corresponding database object. Data comprising the corresponding database objects is provided for rendering the source video content instead of any original pixel information of the source video content so that a virtualized version of the source video content is rendered.

Abstract: Techniques for generating an arbitrary view of an asset are disclosed. In some embodiments, arbitrary view generation includes storing a set of images of an asset, wherein each image comprising at least a subset of the set of images has an overlapping field of view with at least one other image in the subset, and generating an image comprising an arbitrary perspective of the asset at least in part by populating the image comprising the arbitrary perspective with pixels harvested from one or more images comprising the set of images.

Abstract: A machine learning based image processing architecture and associated applications are disclosed herein. In some embodiments, a machine learning framework is trained to learn low level image attributes such as object/scene types, geometries, placements, materials and textures, camera characteristics, lighting characteristics, contrast, noise statistics, etc. Thereafter, the machine learning framework may be employed to detect such attributes in other images and process the images at the attribute level.

Abstract: A machine learning based image processing and generation framework is disclosed. In some embodiments, depth values of an object or asset in a received input image are at least in part determined using a machine learning based framework that is constrained to a known prescribed environment. Determined depth values facilitate generation of other views of the object or asset.

Abstract: Techniques for generating an image are disclosed. In some embodiments, a received input image is transformed to generate an output image using a machine learning based framework that is trained on a constrained set of images. The generated output image comprises an attribute learned by the machine learning based framework from the set of images.

Abstract: Techniques for generating an arbitrary view or perspective of an ensemble scene are disclosed. In some embodiments, in response to a received request for a prescribed perspective of an ensemble scene comprising a plurality of assets, an output image of the ensemble scene for the requested prescribed perspective is generated based at least in part on combining at least a portion of an existing image of each of at least a subset of the plurality of assets.

Abstract: Techniques for generating a prescribed perspective of a scene are disclosed. In some embodiments, a specification of a scene comprising a plurality of objects is specified using object views that are substantially free of perspective distortions, and a prescribed perspective of the scene is at least in part generated by populating the prescribed perspective of the scene with pixels from existing images of the plurality of objects.

Abstract: A machine learning based image processing architecture and associated applications are disclosed herein. In some embodiments, a machine learning framework is trained to learn low level image attributes such as object/scene types, geometries, placements, materials and textures, camera characteristics, lighting characteristics, contrast, noise statistics, etc. Thereafter, the machine learning framework may be employed to detect such attributes in other images and process the images at the attribute level.

Abstract: Techniques for generating aligned, augmented reality views are disclosed. In some embodiments, an estimate of a floorplan of a three-dimensional space is first generated based on one or more received views of the three-dimensional space from a camera located in the three-dimensional space. A camera pose of the camera in the three-dimensional space is next determined based on generating a corrected floorplan from the estimate of the floorplan based on foreknowledge associated with the floorplan. Display of a rendering having the determined camera pose is facilitated in an actual view of the camera, resulting in an aligned, augmented reality view.