Abstract: Methods and systems are disclosed for generating a 3D body mesh. The system receives an image that includes a depiction of a real-world object in a real-world environment. The system applies a first machine learning model to a portion of the image that depicts the real-world object to predict a tensor of heatmaps representing vertex positions of a plurality of triangles of a 3D mesh corresponding to the real-world object. First and second heatmaps of the tensor represent respectively first and second groups of possible coordinates for a first vertex of a first triangle of the plurality of triangles. The system generates the 3D mesh based on the selected subset of the tensor of heatmaps.

Abstract: Methods and systems are disclosed for transferring garments from a real-world object to a virtual object. The system receives, by a client device, an image that includes a depiction of a real-world object having a fashion item in a real-world environment. The system accesses a three-dimensional (3D) avatar model of a human and generates a graphic item corresponding to the fashion item being worn by the real-world object depicted in the image. The system modifies the 3D avatar model of the human based on the graphic item and presents the 3D avatar model that has been modified based on the graphic item within a view of the real-world environment on the client device.

Abstract: Methods and systems are disclosed for transferring garments from one real-world object to another in real time using body landmarks. The system receives a first image that includes a depiction of a first person wearing a fashion item in a first pose. The system obtains a second image that includes a depiction of a second person in a second pose and generates a first set of body landmarks corresponding the first person in the first pose and a second set of body landmarks corresponding the second person wearing in the first pose. The system computes a deviation between the first set of body landmarks and the second set of body landmarks. The system generates a new image that depicts the second person wearing the fashion item worn by the first person based on the deviation between the first set of body landmarks and the second set of body landmarks.

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: 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: 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.