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 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: The present invention discloses a sketch-based shape maintaining tree transformation animation method, comprising: creating a three-dimensional tree model; representing the three-dimensional tree model in the form of a skeleton and a cluster of leaves; selecting an angle of interest, detecting the step (2) canopy silhouette of the 3D tree model, and the transformation animation target canopy silhouette is drawn on the sketch, and the crown silhouette of the 3D tree model is gradually transformed into a transformation animation target crown silhouette, and the crown of the 3D tree model is the transformation of the silhouette is transmitted to the branches to obtain a smooth transformation process of the trees; the transformation process of the crown silhouette and the branches is recorded frame by frame, and the tree transformation animation of the shape is obtained.

Abstract: The present invention discloses a sketch-based shape maintaining tree transformation animation method, comprising: creating a three-dimensional tree model; representing the three-dimensional tree model in the form of a skeleton and a cluster of leaves; selecting an angle of interest, detecting the step (2) canopy silhouette of the 3D tree model, and the transformation animation target canopy silhouette is drawn on the sketch, and the crown silhouette of the 3D tree model is gradually transformed into a transformation animation target crown silhouette , and the crown of the 3D tree model is the transformation of the silhouette is transmitted to the branches to obtain a smooth transformation process of the trees; the transformation process of the crown silhouette and the branches is recorded frame by frame, and the tree transformation animation of the shape is obtained.