Fyusion, Inc.

Krunal Ketan Chande

Stefan Johannes Josef Holzer

Radu Bogdan Rusu

Matteo Munaro

Alexander Jay Bruen Trevor

Abhishek Kar

Stephen David Miller

Pavel Hanchar

Martin Markus Hubert Wawro

Martin Saelzle

Nico Gregor Sebastian Blodow

Pantelis Kalogiros

Chris Beall

Aidas Liaudanskas

Yusuke Tomoto

Mike Penz

Matthias Reso

Milos Vlaski

Luo Yi Tan

Wook Yeon Hwang

Johan Nordin

Nick Stetco

Yuheng Ren

Rodrigo Ortiz-Cayon

Rodrigo Ortiz Cayon

Martin Josef Nikolaus Saelzle

Julius Santiago

Ioannis Spanos

Michelle Jung-Ah Ho

Santi Arano

Srinivas Rao

Raul Dronca

Tobias Bertel

Christian Richardt

Blake McConnell

Provided are mechanisms and processes for scene-aware selection of filters and effects for visual digital media content. In one example, a digital media item is analyzed with a processor to identify one or more characteristics associated with the digital media item, where the characteristics include a physical object represented in the digital media item. Based on the identified characteristics, a digital media modification is selected from a plurality of digital media modifications for application to the digital media item. The digital media modification may then be provided for presentation in a user interface for selection by a user.

Scene-aware selection of filters and effects for visual digital media content

Various embodiments of the present disclosure relate generally to systems and methods for generating multi-view interactive digital media representations in a virtual reality environment. According to particular embodiments, a plurality of images is fused into a first content model and a first context model, both of which include multi-view interactive digital media representations of objects. Next, a virtual reality environment is generated using the first content model and the first context model. The virtual reality environment includes a first layer and a second layer. The user can navigate through and within the virtual reality environment to switch between multiple viewpoints of the content model via corresponding physical movements. The first layer includes the first content model and the second layer includes a second content model and wherein selection of the first layer provides access to the second layer with the second content model.

Virtual reality environment based manipulation of multi-layered multi-view interactive digital media representations

Damage to an object such as a vehicle may be detected and presented based at least in part on image data. In some configurations, image data may be detected by causing the object to pass through a gate or portal on which cameras are located. Alternatively, or additionally, image data may be selected by a user operating a camera and moving around the object. The cameras may capture image data, which may be combined and analyzed to detect damage. Some or all of the image data and/or analysis of the image data may be presented in a viewer, which may allow a user to perform actions such as navigating around the object in a virtual environment, identifying and viewing areas of the object where damage has been detected, and accessing the results of the analysis.

Multi-view interactive digital media representation viewer

Images may be captured at an image capture device mounted on an image capture device gimbal capable of rotating the image capture device around a nodal point in one or more dimensions. Each of the plurality of images may be captured from a respective rotational position. The images may be captured by a designated camera that is not located at the nodal point in one or more of the respective rotational positions. A designated three-dimensional point cloud may be determined based on the plurality of images. The designated three-dimensional point cloud may include a plurality of points each having a respective position in a virtual three-dimensional space.

Enclosed multi-view visual media representation

Images of an undercarriage of a vehicle may be captured via one or more cameras. A point cloud may be determined based on the images. The point cloud may includes points positioned in a virtual three-dimensional space. A stitched image may be determined based on the point cloud by projecting the point cloud onto a virtual camera view. The stitched image may be stored on a storage device.

Vehicle undercarriage imaging

The pose of an object may be estimated based on fiducial points identified in a visual representation of the object. Each fiducial point may correspond with a component of the object, and may be associated with a first location in an image of the object and a second location in a 3D coordinate pace. A 3D skeleton of the object may be determined by connecting the locations in the 3D space, and the object's pose may be determined based on the 3D skeleton.

Object pose estimation in visual data

Novel images may be generated using an image generator implemented on a processor. The image generator may receive as input neural features selected from a neural texture atlas. The image generator may also receive as input one or more position guides identifying position information for a plurality of input image pixels. The novel images may be evaluated using an image discriminator to determine a plurality of optimization values by comparing each of the plurality of novel images with a respective one of a corresponding plurality of input images. Each of the novel images may be generated from a respective camera pose relative to an object identical to that of the respective one of the corresponding plurality of input images. The image generator and the neural features may be updated based on the optimization values and stored on a storage device.

Deferred neural rendering for view extrapolation

According to various embodiments, component information may be identified for each input image of an object. The component information may indicate a portion of the input image in which a particular component of the object is depicted. A viewpoint may be determined for each input image that indicates a camera pose for the input image relative to the object. A three-dimensional skeleton of the object may be determined based on the viewpoints and the component information. A multi-view panel corresponding to the designated component of the object that is navigable in three dimensions and that the portions of the input images in which the designated component of the object is depicted may be stored on a storage device.

Extraction of standardized images from a single view or multi-view capture

The pose of an object may be estimated based on fiducial points identified in a visual representation of the object. Each fiducial point may correspond with a component of the object, and may be associated with a first location in an image of the object and a second location in a 3D space. A 3D skeleton of the object may be determined by connecting the locations in the 3D space, and the object's pose may be determined based on the 3D skeleton.

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San Francisco, CA, United States of America

Krunal Ketan Chande