Abstract: A computer-implemented method for recovering a three-dimensional (3D) mesh of N humans in a 3D scene, includes: receiving a two-dimensional (2D) image of the scene captured by an image capturing device, the 2D image including a plurality of regions; by one or more processors, encoding the received image and extracting features for each of the plurality of regions; by one or more processors, detecting the N humans in N respective regions among the plurality of regions; by one or more processors, based on the features in the N respective regions and the features for each of the plurality of regions, determining distribution parameters for distributions of body model and depth parameters for each of the N detected humans using a probabilistic network; providing the distribution parameters for the body model parameters for each of the N detected humans to a 3D parametric model for generating N 3D meshes.

Abstract: A method for recovering a 3D mesh of N humans in a 3D scene comprises: encoding a 2D image from an image capturing device to extract embedded features for each of a plurality of regions; detecting N humans in N respective regions among the plurality of regions; processing the embedded features in the N respective regions and the embedded features for each of the plurality of regions to predict body model and depth parameters using a decoder comprising a cross-attention module; providing the predicted body model parameters to a 3D parametric model for generating 3D meshes; and placing the generated 3D meshes at respective 3D spatial locations based on the predicted depth parameters.

Abstract: A training system includes: a model; and a training module configured to: construct a first pair of images of at least a first portion of a first human captured at different times; construct a second pair of images of at least a second portion of a second human captured at the same time from different points of view; input the first and second pairs of images to the model; the model configured to: generate first and second reconstructed images of the at least the first portion of the first human based on the first and second pairs, respectively, and the training module is configured to selectively adjust one or more parameters of the model based on: the first reconstructed image and the second reconstructed image.