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.

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 composite object are disclosed. In some embodiments, a specification of a composite object comprising a plurality of objects is specified using object views of the plurality of objects that comprise a fixed depth, and a prescribed perspective of the composite object is at least in part generated by populating the prescribed perspective of the composite object 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 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: 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 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: Adaptive sampling of pixels is disclosed. In some embodiments, convergence of pixels of a rendering is evaluated by removing texture from the rendering to eliminate non-convergence due to texture complexity and determining whether pixels values satisfy convergence criteria associated with pixel sampling. Those pixels of the rendering that have not converged are sampled with one or more additional samples. The completed rendering is output. Pixels of the completed rendering satisfy the convergence criteria and are not uniformly sampled.

Abstract: Improved techniques for generating video content are disclosed. In some embodiments, it is determined whether a frame comprising a scene is an independent frame or a predictive frame. In the event that the frame is an independent frame, the frame is encoded independently. In the event that the frame is a predictive frame, block differences and motion vectors are encoded, wherein motion vectors are determined from a known three-dimensional model and time evolution of the scene.

Abstract: Techniques for generating an ensemble image are disclosed. In some embodiments, images associated with a plurality of independent scenes are translated to a prescribed origin, translated images associated with each independent scene are transformed into a prescribed perspective; and pixels of an image array having the prescribed perspective that is associated with each independent scene is populated with corresponding pixels from the transformed translated images associated with that independent scene. An ensemble image comprising the prescribed perspective is at least in part generated by combining at least some pixels of image arrays associated with the plurality of independent scenes.

Abstract: A content generation platform is generally described herein. More specifically, interactive image generation and techniques and features thereof are disclosed herein. One or more sets of images of a scene are captured in an imaging studio. The captured one or more sets of images of the scene are processed using one or more machine learning based networks to generate an interactive image of the scene comprising a plurality of interactive features. One or more of the plurality of interactive features of the generated interactive image may be modified or edited according to user preferences.

Abstract: A content generation platform is generally described herein. More specifically, interactive image generation and techniques and features thereof are disclosed herein. One or more sets of images of a scene are captured in an imaging studio. The captured one or more sets of images of the scene are processed using one or more machine learning based networks to generate an interactive image of the scene comprising a plurality of interactive features. One or more of the plurality of interactive features of the generated interactive image may be modified or edited according to user preferences.

Abstract: A content generation platform is generally described herein. More specifically, interactive image generation and techniques and features thereof are disclosed herein. An arbitrary input image captured in an unknown environment is processed using one or more machine learning based networks to generate an interactive reconstruction or floorplan of the input image.

Abstract: A content generation platform is generally described herein. More specifically, interactive image generation and techniques and features thereof are disclosed herein. One or more sets of images of a scene are captured in an imaging studio. The captured one or more sets of images of the scene are processed using one or more machine learning based networks to generate an interactive image of the scene comprising a plurality of interactive features. One or more of the plurality of interactive features of the generated interactive image may be modified or edited according to user preferences.

Abstract: A content generation platform is generally described herein. More specifically, interactive image generation and techniques and features thereof are disclosed herein. One or more sets of images of a scene are captured in an imaging studio. The captured one or more sets of images of the scene are processed using one or more machine learning based networks to generate an interactive image of the scene comprising a plurality of interactive features. One or more of the plurality of interactive features of the generated interactive image may be modified or edited according to user preferences.

Abstract: A content generation platform is generally described herein. More specifically, interactive image generation and techniques and features thereof are disclosed herein. One or more sets of images of a scene are captured in an imaging studio. The captured one or more sets of images of the scene are processed using one or more machine learning based networks to generate an interactive image of the scene comprising a plurality of interactive features. One or more of the plurality of interactive features of the generated interactive image may be modified or edited according to user preferences.

Abstract: A content generation platform is generally described herein. More specifically, interactive image generation and techniques and features thereof are disclosed herein. One or more sets of images of a scene are captured in an imaging studio. The captured one or more sets of images of the scene are processed using one or more machine learning based networks to generate an interactive image of the scene comprising a plurality of interactive features. One or more of the plurality of interactive features of the generated interactive image may be modified or edited according to user preferences.