Abstract: Methods and systems are disclosed for performing real-time stylizing operations. The system receives an image that includes a depiction of a whole body of a real-world person. The system applies a machine learning model to the image to generate a stylized version of the whole body of the real-world person corresponding to a given style, the machine learning model being trained using training data to establish a relationship between a plurality of training images depicting synthetically rendered whole bodies of persons and corresponding ground-truth stylized versions of the whole bodies of the persons of the given style. The system replaces the depiction of the whole body of the real-world person in the image with the generated stylized version of the whole body of the real-world person.

Abstract: Aspects of the present disclosure involve a system and a method for performing operations comprising: receiving a plurality of bone scale coefficients each corresponding to respective bones of a skeleton model; receiving a plurality of joint angle coefficients that collectively define a pose for the skeleton model; generating the skeleton model based on the received bone scale coefficients and the received joint angle coefficients; generating a base surface based on the plurality of bone scale coefficients; generating an identity surface by deformation of the base surface; and generating the 3D body model by mapping the identity surface onto the posed skeleton model.

Abstract: This disclosure relates to reconstructing three-dimensional models of objects from two-dimensional images. According to a first aspect, this specification describes a computer implemented method for creating a three-dimensional reconstruction from a two-dimensional image, the method comprising: receiving a two-dimensional image; identifying an object in the image to be reconstructed and identifying a type of said object; spatially anchoring a pre-determined set of object landmarks within the image; extracting a two-dimensional image representation from each object landmark; estimating a respective three-dimensional representation for the respective two-dimensional image representations; and combining the respective three-dimensional representations resulting in a fused three-dimensional representation of the object.

Abstract: Aspects of the present disclosure involve a system for presenting AR items. The system performs operations including: receiving an image that includes a depiction of a first real-world body part in a real-world environment; applying a machine learning technique to the image to generate a plurality of dense outputs each associated with a respective pixel of a plurality of pixels in the image; applying a first task-specific decoder to the plurality of dense outputs to identify a pixel corresponding to a center of the first real-world body part; applying a second task-specific decoder using the identified pixel to retrieve a 3D rotation, translation and scale of first real-world body part from the plurality of dense outputs; modifying an AR object based on the 3D rotation, translation, and scale of first real-world body part; and modifying the image to include a depiction of the modified AR object.

Abstract: Aspects of the present disclosure involve a system for presenting AR items. The system performs operations including: receiving an image that includes a depiction of a first real-world body part in a real-world environment; applying a machine learning technique to the image to generate a plurality of dense outputs each associated with a respective pixel of a plurality of pixels in the image; applying a first task-specific decoder to the plurality of dense outputs to identify a pixel corresponding to a center of the first real-world body part; applying a second task-specific decoder using the identified pixel to retrieve a 3D rotation, translation and scale of first real-world body part from the plurality of dense outputs; modifying an AR object based on the 3D rotation, translation, and scale of first real-world body part; and modifying the image to include a depiction of the modified AR object.

Abstract: This disclosure relates to reconstructing three-dimensional models of objects from two-dimensional images. According to a first aspect, this specification describes a computer implemented method for creating a three-dimensional reconstruction from a two-dimensional image, the method comprising: receiving a two-dimensional image; identifying an object in the image to be reconstructed and identifying a type of said object; spatially anchoring a pre-determined set of object landmarks within the image; extracting a two-dimensional image representation from each object landmark; estimating a respective three-dimensional representation for the respective two-dimensional image representations; and combining the respective three-dimensional representations resulting in a fused three-dimensional representation of the object.

Abstract: 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.

Abstract: Aspects of the present disclosure involve a system for presenting AR items. The system performs operations including: receiving an image that includes a depiction of a first real-world body part in a real-world environment; applying a machine learning technique to the image to generate a plurality of dense outputs each associated with a respective pixel of a plurality of pixels in the image; applying a first task-specific decoder to the plurality of dense outputs to identify a pixel corresponding to a center of the first real-world body part; applying a second task-specific decoder using the identified pixel to retrieve a 3D rotation, translation and scale of first real-world body part from the plurality of dense outputs; modifying an AR object based on the 3D rotation, translation, and scale of first real-world body part; and modifying the image to include a depiction of the modified AR object.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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

Abstract: 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.

Abstract: This disclosure relates to reconstructing three-dimensional models of objects from two-dimensional images. According to a first aspect, this specification describes a computer implemented method for creating a three-dimensional reconstruction from a two-dimensional image, the method comprising: receiving a two-dimensional image; identifying an object in the image to be reconstructed and identifying a type of said object; spatially anchoring a pre-determined set of object landmarks within the image; extracting a two-dimensional image representation from each object landmark; estimating a respective three-dimensional representation for the respective two-dimensional image representations; and combining the respective three-dimensional representations resulting in a fused three-dimensional representation of the object.

Abstract: 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

Abstract: Aspects of the present disclosure involve a system and a method for performing operations comprising: receiving a plurality of bone scale coefficients each corresponding to respective bones of a skeleton model; receiving a plurality of joint angle coefficients that collectively define a pose for the skeleton model; generating the skeleton model based on the received bone scale coefficients and the received joint angle coefficients; generating a base surface based on the plurality of bone scale coefficients; generating an identity surface by deformation of the base surface; and generating the 3D body model by mapping the identity surface onto the posed skeleton model.