Systems and methods are provided for simulating medical images based on previously acquired data and a defined imaging protocol. In an example, a method includes generating a simulated medical image of a patient via virtual imaging based on previously obtained medical images and a scan intent of the virtual imaging, and outputting an imaging protocol based on a virtual protocol of the virtual imaging.

The subject technology generates depth data using a machine learning model based at least in part on captured image data from at least one camera of a client device. The subject technology applies, to the captured image data and the generated depth data, a 3D effect based at least in part on an augmented reality content generator. The subject technology generates a depth map using at least the depth data. The subject technology generates a packed depth map based at least in part on the depth map, the generating the packed depth map. The subject technology converts a single channel floating point texture to a raw depth map. The subject technology generates multiple channels based at least in part on the raw depth map. The subject technology generates a segmentation mask based at least on the captured image data. The subject technology performs background inpainting and blurring of the captured image data using at least the segmentation mask to generate background inpainted image data.

Disclosed are systems and methods for generating large data sets for training deep learning networks (DLNs) for 3D measurements extraction from 2D images taken using a mobile device camera. The method includes the steps of receiving a 3D model of a 3D object; extracting spatial features from the 3D model; generating a first type of augmentation data for the 3D model, such as but not limited to skin color, face contour, hair style, virtual clothing, and/or lighting conditions; augmenting the 3D model with the first type of augmentation data to generate an augmented 3D model; generating at least one 2D image from the augmented 3D model by performing a projection of the augmented 3D model onto at least one plane; and generating a training data set to train the deep learning network (DLN) for spatial feature extraction by aggregating the spatial features and the at least one 2D image.

A system allows a user to select designs for apparel and preview these designs before manufacture. The previews provide photorealistic visualizations in two or three dimensions, and can be presented on a computer display, projected on a screen, or presented in virtual reality (e.g., via headset), or augmented reality. The system can also include a projection system that projects the new designs onto garments on mannequins in a showroom. The garments and the new designs projected onto the garments have a three-dimensional appearance as if the garments having the new designs are worn by people. Software and lasers are used in finishing the garments to produce garments with the new designs and the finished garments have an appearance of the three-dimensional appearance of the garments and new designs on the mannequins with the new designs projected onto the garments by the projection system.

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.

Embodiments disclosed herein mitigate technological barriers in preparing, sourcing, and exporting 3D assets with practical applications for journalism. According to one embodiment, a computer-implemented method for generating a three-dimensional map is provided. The method includes displaying a two-dimensional region of a map on a graphical user interface (GUI), wherein the map is displayed along longitudinal axis and a latitudinal axis. The method includes obtaining, through the GUI, a first input from a user device, the first input comprising an indication of a selected sub-region of the two-dimensional region of the map, wherein the selected sub-region comprises a plurality of pixels having longitudinal coordinates bounded by a first longitude coordinate and a second longitude coordinate along the longitudinal axis and latitudinal coordinates bounded by a first latitude coordinate and a second latitude coordinate along the latitudinal axis. The method includes obtaining, from a first data source, elevation coordinates along a lateral axis corresponding to the longitudinal coordinates and the latitudinal coordinates for each of the plurality of pixels. The method includes generating a three-dimensional mesh of the selected sub-region, wherein the three-dimensional mesh comprises a plurality of vertices corresponding to the elevation coordinates, the longitudinal coordinates, and the latitudinal coordinates of the plurality of pixels and a plurality of edges connecting the vertices. The method includes displaying the three-dimensional mesh on the GUI.

In the disclosed systems and methods for competitive scene completion, an application displays an initial scene of a first scene completion challenge and displays within the initial scene a plurality of markers. Each of the markers has corresponding predefined designated coordinates and corresponds to a furnishing unit type. The application also displays virtual furnishing units that match the furnishing unit type of a corresponding marker. User selection of virtual furnishing units results in display of a three-dimensional rendition of the selected units at corresponding coordinates within the scene, thereby creating an augmented scene that comprises the initial scene with three-dimensional renditions of selected virtual furnishing units. The augmented scene or user selections of virtual furnishing units is submitted to a remote server and the user is provided with a reward.

Procedural model digital content editing techniques are described that overcome the limitations of conventional techniques to make procedural models available for interaction by a wide range of users without requiring specialized knowledge and do so without “breaking” the underlying model. In the techniques described herein, an inverse procedural model system receives a user input that specifies an edit to digital content generated by a procedural model. Input parameters from these candidate input parameters are selected by the system which cause the digital content generated by the procedural model to incorporate the edit.

A messaging system performs neural network hair rendering for images provided by users of the messaging system. A method of neural network hair rendering includes processing a three-dimensional (3D) model of fake hair and a first real hair image depicting a first person to generate a fake hair structure, and encoding, using a fake hair encoder neural subnetwork, the fake hair structure to generate a coded fake hair structure. The method further includes processing, using a cross-domain structure embedding neural subnetwork, the coded fake hair structure to generate a fake and real hair structure, and encoding, using an appearance encoder neural subnetwork, a second real hair image depicting a second person having a second head to generate an appearance map. The method further includes processing, using a real appearance renderer neural subnetwork, the appearance map and the fake and real hair structure to generate a synthesized real image.

An exemplary virtual reality media system presents a field of view of an immersive virtual reality world on a display screen of a media player device associated with a user. The field of view includes content of the immersive virtual reality world and dynamically changes in response to user input provided by the user as the user experiences the immersive virtual reality world. Additionally, the virtual reality media system detects that a gaze of the user is directed for a predetermined amount of time at a gaze target included within the field of view. In response to the detection, the virtual reality media system presents an interactive user interface associated with the gaze target. In some examples, the interactive user interface is presented within the field of view together with the content of the immersive virtual reality world. Corresponding methods and systems are also described.