首页|单视角三维服装重建的民族风格表征学习

单视角三维服装重建的民族风格表征学习

扫码查看
原文链接
  • NETL
  • NSTL
  • 万方数据
  • 维普
针对民族服装风格多样、款式和配饰复杂，导致单视角三维服装重建结构不完整、风格不准确和局部特征模糊等问题，为了学习和映射民族服装的潜在风格特征，提出一种单视角三维服装重建的民族风格表征学习方法。首先结合定义的民族服装形状风格和几何拓扑构建形状表征，学习形状潜在特征;然后基于区域定位和关键点，并融合定义的款式风格、着装部位进行款式表征，得到局部感知区域图，以学习款式潜在特征;再联合形状、款式特征以及定义的对称损失函数，隐式重建出民族服装的初步模型;最后在卷积网络中引入图像的超像素特征、Branch分支和配饰的语义解析建立配饰表征，并编码UV位置图到特征空间，生成民族服装的最终模型。在自定义的少数民族服装数据集上的实验结果表明，所提方法的倒角距离和法线余弦距离分别为1。732和0。13，较已有方法降低11％和18％，能够提高单视角三维民族服装重建的精度，生成具有民族风格的三维服装模型。
Ethnic Style Representation Learning for Single-View 3D Garment Reconstruction
To address the problem of incomplete structure,inaccurate style and fuzzy local feature caused by the diversity and complexity of styles and accessories for minority clothing in single-view three-dimensional garment reconstruction,an ethnic style representation learning method is proposed to learn and map the under-lying style feature.Firstly,the shape underlying feature is learned by constructed shape representation using the defined shape style and geometric-topology of minority clothing.Secondly,the style representation is con-ducted based on regional location and key points by fusing the defined clothing style and dressed parts to ob-tain the local perception region maps.Then,combining the shape feature,the style feature and the defined symmetric loss function to implicitly reconstruct the preliminary model.Finally,the superpixel feature of im-age,a Branch network and the semantic parsing of accessories are added to the basis of convolutional network to establish accessory representation by encoding UV position map to generate the final model.The experi-mental results on minority garment dataset show that the chamfer distance and normal cosine distance error are 1.732 and 0.13 respectively,which reduce by 11％and 18％.The proposed method can improve the accuracy of three-dimensional ethnic clothing reconstruction,which generates three-dimensional garment model with eth-nic style.

minority clothing imagethree-dimensional garment reconstructionrepresentation learningshape representationimplicit reconstructionsemantic parsing

张瑀卿、刘骊、付晓东、刘利军、彭玮

展开 >

昆明理工大学信息工程与自动化学院 昆明 650500

昆明理工大学云南省计算机技术应用重点实验室 昆明 650500

少数民族服装图像 三维服装重建 表征学习 形状表征 隐式重建 语义解析

国家自然科学基金国家自然科学基金国家自然科学基金云南省中青年学术和技术带头人后备人才培养计划

622620366186203661962030202005AC160036

2024

10.3724/SP.J.1089.2024.19815

计算机辅助设计与图形学学报
中国计算机学会

计算机辅助设计与图形学学报

CSTPCD北大核心
影响因子：0.892
ISSN：1003-9775
年,卷(期)：2024.36(2)
  • 31