Abstract: A method of automatically generating human-body parameters using machine learning, including the following steps: initializing a converting program, inputs of which are accurate human-body parameters and outputs of which are general body-shape descriptions; inputting several groups of accurate human-body parameters into the converting program, so as to obtain various combinations of general body-shape descriptions, which are to be used as training sets for subsequent steps; carrying out training through machine learning by using the previously obtained training sets, to obtain a mapping relationship between the general body-shape descriptions and parameters of a 3D human body model; recording gender, height, and weight information from a user and the user's responses to a series of preset general descriptive questions about body shape, and using the previously obtained mapping relationship, to output accurate human-body parameters representing an actual human body of the user.

Abstract: A method of garment deformation based on Laplacian deformation of a human body, including the following steps: inputting polygonal mesh models of the human body and the garment; discretizing non-homogeneous mesh models of the human body and the garment inputted in the first step; clustering all the discretized mesh vertices, to reduce a number of vertices and to form a set of homogeneous discrete vertices; constructing Laplacian matrices of the human body and the garment; preprocessing and solving inverse matrices; editing by using the human body mesh as a control vertex, to drive a real-time smooth deformation of the garment mesh; and mapping a deformed and simplified mesh back to a mesh space of an original resolution to obtain deformed human body and garment mesh models.

Abstract: A method of automatically generating human-body parameters using machine learning, including the following steps: initializing a converting program, inputs of which are accurate human-body parameters and outputs of which are general body-shape descriptions; inputting several groups of accurate human-body parameters into the converting program, so as to obtain various combinations of general body-shape descriptions, which are to be used as training sets for subsequent steps; carrying out training through machine learning by using the training sets obtained from Step (1), to obtain a mapping relationship between the general body-shape descriptions and parameters of a 3D human body model; recording gender, height, and weight information from a user and the user's responses to a series of preset general descriptive questions about body shape, and using the mapping relationship obtained from Step (3), to output accurate human-body parameters representing an actual human body of the user.

Abstract: A method of garment deformation based on Laplacian deformation of a human body, including the following steps: inputting polygonal mesh models of the human body and the garment; discretizing non-homogeneous mesh models of the human body and the garment inputted in the first step; clustering all the discretized mesh vertices, to reduce a number of vertices and to form a set of homogeneous discrete vertices; constructing Laplacian matrices of the human body and the garment; preprocessing and solving inverse matrices; editing by using the human body mesh as a control vertex, to drive a real-time smooth deformation of the garment mesh; and mapping a deformed and simplified mesh back to a mesh space of an original resolution to obtain deformed human body and garment mesh models.