A method for generating virtual pseudo three dimensional 360 degree outputs from 2D images of an object 102 is provided. An image viewer plane of the object 102 in the 3D image to be rendered on a user device 108 is detected using an augmented reality technique. An image viewer plane is placed facing the user device 108 rendering ‘Image 0’ and movement coordinates of the user device 108 with respect to the image viewer plane is detected to calculate the virtual pseudo 3D image set to be displayed based on at least one angle of view by performing interpolation between two consecutive virtual pseudo 3D images. The image viewer plane is changed with respect to the movement of the user device 108 to change the virtual pseudo 3D image and the interpolated virtual pseudo 3D image on the plane and that image is displayed as an augmented reality object in real-time to the user device 108.

Embodiments provide systems and methods for three-dimensional building generation from machine learning and topological models. The method uses topology models that are converted into vertices and edges. A BGAN (Building generative adversarial network) is used to create fake vertices/edges. The BGAN is then used to generate random samples from seen sample of different structures of building based on relationship of vertices and edges. The embeddings are then fed into a machine trained network to create a digital structure from the image.

Embodiments provide systems and methods for three-dimensional building generation from machine learning and topological models. The method uses topology models that are converted into vertices and edges. A BGAN (Building generative adversarial network) is used to create fake vertices/edges. The BGAN is then used to generate random samples from seen sample of different structures of building based on relationship of vertices and edges. The embeddings are then fed into a machine trained network to create a digital structure from the image.

A system for generating three-dimensional (3D) objects from two-dimensional (2D) images of garments is presented. The system includes a data module configured to receive a 2D image of a selected garment and a target 3D model. The system further includes a computer vision model configured to generate a UV map of the 2D image of the selected garment. The system moreover includes a training module configured to train the computer vision model based on a plurality of 2D training images and a plurality of ground truth (GT) panels for a plurality of 3D training models. The system furthermore includes a 3D object generator configured to generate a 3D object corresponding to the selected garment based on the UV map generated by a trained computer vision model and the target 3D model. A related method is also presented.

Described are systems and method directed to generation of a dimensionally accurate three-dimensional (“3D”) body model of a body, such as a human body, based on two-dimensional (“2D”) images of that body. A user may use a 2D camera, such as a digital camera typically included in many of today's portable devices (e.g., cell phones, tablets, laptops, etc.) and obtain a series of 2D body images of their body from different directions with respect to the camera. The 2D body images may then be used to generate a plurality of predicted body parameters corresponding to the body represented in the 2D body images. Those predicted body parameters may then be further processed to generate a dimensionally accurate 3D model of the body of the user.

Described are systems and method directed to generation of a dimensionally accurate three-dimensional (“3D”) body model of a body, such as a human body, based on two-dimensional (“2D”) images of that body. A user may use a 2D camera, such as a digital camera typically included in many of today's portable devices (e.g., cell phones, tablets, laptops, etc.) and obtain a series of 2D body images of their body from different directions with respect to the camera. The 2D body images may then be used to generate a plurality of predicted body parameters corresponding to the body represented in the 2D body images. Those predicted body parameters may then be further processed to generate a dimensionally accurate 3D model of the body of the user.

A method of video playback includes obtaining a 3D reconstruction of at least part of an environment, obtaining video footage of an activity within that at least part of the environment, obtaining data indicating a position and orientation of a first camera within the at least part of the environment that recorded the obtained video footage, and rendering a view of the video footage in alignment with a corresponding view of at least part of the 3D reconstruction, responsive to the position and orientation of the first camera within the at least part of the environment.

A method of video playback includes obtaining a 3D reconstruction of at least part of an environment, obtaining video footage of an activity within that at least part of the environment, obtaining data indicating a position and orientation of a first camera within the at least part of the environment that recorded the obtained video footage, and rendering a view of the video footage in alignment with a corresponding view of at least part of the 3D reconstruction, responsive to the position and orientation of the first camera within the at least part of the environment.

A method for generating a three-dimensional (3D) model of an object includes: capturing images of the object from a plurality of viewpoints, the images including color images; generating a 3D model of the object from the images, the 3D model including a plurality of planar patches; for each patch of the planar patches: mapping image regions of the images to the patch, each image region including at least one color vector; and computing, for each patch, at least one minimal color vector among the color vectors of the image regions mapped to the patch; generating a diffuse component of a bidirectional reflectance distribution function (BRDF) for each patch of planar patches of the 3D model in accordance with the at least one minimal color vector computed for each patch; and outputting the 3D model with the BRDF for each patch.

A method for generating a three-dimensional (3D) model of an object includes: capturing images of the object from a plurality of viewpoints, the images including color images; generating a 3D model of the object from the images, the 3D model including a plurality of planar patches; for each patch of the planar patches: mapping image regions of the images to the patch, each image region including at least one color vector; and computing, for each patch, at least one minimal color vector among the color vectors of the image regions mapped to the patch; generating a diffuse component of a bidirectional reflectance distribution function (BRDF) for each patch of planar patches of the 3D model in accordance with the at least one minimal color vector computed for each patch; and outputting the 3D model with the BRDF for each patch.