Research And Application Of 3D Human Body Digital Representation

In recent years,with the growth of demand in applications such as virtual try-on,customized design and body health monitoring,simple and effective 3D human body modeling techniques have caught more and more attention.Traditional 3D human body reconstruction techniques based on large laser scanner provides a great deal of body data for 3D human body digital representation,the machine learning technologies in the ascending stage,especially deep learning,provide a new theoretical and algorithmic tool for 3D human body digital representation.Generally speaking,3D human digital representation mainly involves the following aspects:data preprocessing,human shape representation space and efficient 3D human body reconstruction.Data preprocessing refers to techniques such as noise filtering and posture standardization of the scanned 3D objects in order to obtain a high-quality and easy to use human body data set;The human body shape representation space refers to the statistical modeling of the 3D human body data to obtain the parameterized representation model in its compressed form,which is convenient for data storage,transmission and processing;Efficient 3D human body reconstruction refers to the prediction and analysis of the human body shape in pictures or videos with the assistance of the human body shape representation space,which is applied to virtual try-on or personalized clothing customization.In order to provide new theoretical basis and technique methods for 3D human body digital representation,this paper has conducted investigations on the above aspects and the main contributions are as follows:(1)Aiming at the problem of mesh denoising in the process of object reconstruction based on 3D scanner,a propagated mesh normal filtering algorithm is proposed.By examining the basic principle of bilateral filtering,this algorithm redefines the weight design of the filtering operator in the manifold structure of the mesh surface,so that it can use the accumulated normal difference to calculate the distance measure in the Gaussian kernel function along the shortest path on the mesh surface.In general,the algorithm supplements the loopholes in the principle of mesh filtering,and enhances the interpretability of mesh surface filtering algorithm,which can effectively maintain the original details of the mesh while filtering the surface noise.(2)Aiming at the problem of standardizing human posture,this paper aims to provide a posture standardization technique suitable for all non-rigid objects.This method is based on the shape deformation framework in digital geometry,and can unfold any non-rigid 3D object into a standard posture.The proposed model can promote all non-adjacent vertex pairs in the mesh to move away during the deformation process,and the local deformation is limited to the regularization strategy of preserving rigidity energy,which effectively protects the local details of 3D object and greatly reduces its own geometric distortion.Under the framework of alternating iterative algorithm optimization,the solution of this model only needs to solve the linear equations,which is simple and efficient.Implementing the parallel strategy within the iterative process further increases the computational effectiveness of the algorithm,and adopting the cascade strategy ensures its robustness.(3)Aiming at the problem of 3D human shape representation space,this paper establishes a new statistical model of human shape representation from the perspective of sparse representation.Compared with the traditional statistical model based on principal component analysis(PCA),our method has a higher compression rate,lower shape approximation error,and reduces the pressure of storing and transmitting 3D human body data.First,the human body mesh is divided into a large number of training signals for sparse representation learning.Then use the online L0 algorithm to learn the dictionary matrix and sparse representation coefficients that can reconstruct all the training signals in the original human body data set.Finally,the reconstructed signal is used to assemble the original three-dimensional human body.Further experiments found that the learned dictionary matrix has approximate generalization and can reconstruct other surface that are different from the original human body mesh.(4)Aiming at the efficient 3D human body reconstruction,this paper attempts to reconstruct the 3D human body shape from two orthogonal human body silhouettes(ie,front and side views).By using supervised learning,the convolutional neural network(CNN)architecture proposed in this paper can not only automatically extract the discriminative features from the front and side view,but also accurately extract the mixed features between them through fusion module,and uses the fully connected layer to establish the mapping function between the mixed feature and the human body shape representation space coefficient.Then using the learned mapping function and the human body shape statistical model,such as the shape statistical model based on PCA or sparse representation,to reconstruct a high-precision 3D human body mesh.Finally,this technology is applied to the field of automated clothing customization.