Snap Inc.

Georgios Papandreou

Riza Alp Guler

Brian Fulkerson

Iason Kokkinos

Haoyang Wang

Matan Zohar

Yanli Zhao

Himmy Tam

Madiyar Aitbayev

Thomas Muttenthaler

Frank Lu

Examples disclosed herein relate to the use of shared pose data in extended reality (XR) tracking. A communication link is established between a first XR device and a second XR device. The second XR device is worn by a user. The first XR device receives pose data of the second XR device via the communication link and captures an image of the user. The user is identified based on the image and the pose data.

Extended reality tracking using shared pose data

Methods and systems are disclosed for generating a body mesh from a single image. The system predicts both a volumetric reconstruction tensor of the monocular image and a pose of an object by applying a first machine learning model to a monocular image. The system identifies a portion of the pose of the object that corresponds to a point in a canonical space associated with a set of position encoding information. The system obtains a point of the volumetric reconstruction tensor corresponding to the identified portion of the pose. The system classifies the obtained point as being inside or outside of a canonical volume by applying a second machine learning model to the obtained point of the volumetric reconstruction tensor together with the set of position encoding information. The system generates a three-dimensional (3D) mesh representing the object in the canonical space.

Body mesh reconstruction from RGB image

Methods and systems are disclosed for performing operations comprising: receiving a video that includes a depiction of a real-world object; generating a three-dimensional (3D) body mesh associated with the real-world object that tracks movement of the real-world object across frames of the video; obtaining an external mesh associated with an augmented reality element; automatically establishing a correspondence between the 3D body mesh associated with the real-world object and the external mesh; deforming the external mesh based on movement of the real-world object and the established correspondence with the 3D body mesh; and modifying the video to include a display of the augmented reality element based on the deformed external mesh.

Deforming custom mesh based on body mesh

Aspects of the present disclosure involve a system and a method for performing operations comprising: receiving a two-dimensional continuous surface representation of a three-dimensional object, the continuous surface comprising a plurality of landmark locations; determining a first set of soft membership functions based on a relative location of points in the two-dimensional continuous surface representation and the landmark locations; receiving a two-dimensional input image, the input image comprising an image of the object; extracting a plurality of features from the input image using a feature recognition model; generating an encoded. feature representation of the extracted features using the first set of soft membership functions; generating a dense feature representation of the extracted features from the encoded representation using a second set of soft membership functions; and processing the second set of soft membership functions and dense feature representation using a neural image decoder model to generate an output image.

Image generation using surface-based neural synthesis

Methods and systems are disclosed for performing operations for applying augmented reality elements to a person depicted in an image. The operations include receiving an image that includes data representing a depiction of a person; extracting a portion of the image; applying a first machine learning model stage to the portion to predict a depth of a point of interest for the data representing the depiction of the person; applying a second machine learning model stage to the portion of the image to predict a relative depth of each pixel in the portion of the image to the predicted depth of the point of interest; generating dense depth reconstruction of the data representing the depiction of the person based on outputs of the first and second stages of the machine learning model; and applying one or more AR elements to the image based on the dense depth reconstruction.

Pixel depth determination for object

This specification relates to reconstructing three-dimensional (3D) scenes from two-dimensional (2D) images using a neural network. According to a first aspect of this specification, there is described a method for creating a three-dimensional reconstruction of a scene with multiple objects from a single two-dimensional image, the method comprising: receiving a single two-dimensional image; identifying all objects in the image to be reconstructed and identifying the type of said objects; estimating a three-dimensional representation of each identified object; estimating a three-dimensional plane physically supporting all three-dimensional objects; and positioning all three-dimensional objects in space relative to the supporting plane.

Scene reconstruction in three-dimensions from two-dimensional images

Methods and systems are disclosed for performing operations for applying augmented reality elements to a person depicted in an image. The operations include receiving an image that includes data representing a depiction of a person; generating a segmentation of the data representing the person depicted in the image; extracting a portion of the image corresponding to the segmentation of the data representing the person depicted in the image; applying a machine learning model to the portion of the image to predict a surface normal tensor for the data representing the depiction of the person, the surface normal tensor representing surface normals of each pixel within the portion of the image; and applying one or more augmented reality (AR) elements to the image based on the surface normal tensor.

Surface normals for pixel-aligned object

Systems and methods for reconstructing 3D models of human bodies from 2D images that counts for perspective and/or distortion effects are provided. The systems and methods include reconstructing a three-dimensional model of an object in a three-dimensional scene from a two-dimensional image comprising an image of the object. The systems and methods include determining an absolute depth of a key point of the object in the image; determining, using the absolute depth of the key point, a three-dimensional position of the key point in the three-dimensional scene; generating, using a neural network, a three-dimensional representation of the object, the three-dimensional representation comprising mesh nodes defined in a coordinate system relative to the key point; and positioning the three-dimensional representation of the object in the scene based on the position of the key point by applying a position dependent rotation to the three-dimensional object.

3D object model reconstruction from 2D images

Aspects of the present disclosure involve a system and a method for performing operations comprising: receiving a two-dimensional continuous surface representation of a three-dimensional object, the continuous surface comprising a plurality of landmark locations; determining a first set of soft membership functions based on a relative location of points in the two-dimensional continuous surface representation and the landmark locations; receiving a two-dimensional input image, the input image comprising an image of the object; extracting a plurality of features from the input image using a feature recognition model; generating an encoded feature representation of the extracted features using the first set of soft membership functions; generating a dense feature representation of the extracted features from the encoded representation using a second set of soft membership functions; and processing the second set of soft membership functions and dense feature representation using a neural image decoder model to generate an output image.

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London, United Kingdom

Georgios Papandreou