论文标题
Sizer：用于解析3D服装和学习尺寸敏感3D服装的数据集和模型
SIZER: A Dataset and Model for Parsing 3D Clothing and Learning Size Sensitive 3D Clothing
论文作者
论文摘要
尽管存在从真实数据中学到的3D服装模型，但没有任何方法可以预测服装变形作为服装尺寸的函数。在本文中，我们介绍了Sizernet，以预测以人体形状和服装尺寸参数为条件的3D服装，而Parsernet可以从输入网眼中的单个通行证中推断出衣服和形状的衣服。 Sizernet允许估计和可视化各种尺寸服装的敷料效果，而Parsernet允许直接编辑输入网格的衣服，从而消除了扫描细分的需求，这本身就是一个具有挑战性的问题。为了学习这些型号，我们介绍了服装尺寸变化的Sizer数据集，其中包括$ 100 $不同的受试者，穿着各种尺寸的休闲服装，总计约为2000张扫描。该数据集包括扫描，对SMPL型号的注册，在衣服零件中分割的扫描，服装类别和尺寸标签。与在Sizer上训练的基线方法相比，我们的实验显示出更好的解析精度和大小预测。代码，模型和数据集将出于研究目的发布。
While models of 3D clothing learned from real data exist, no method can predict clothing deformation as a function of garment size. In this paper, we introduce SizerNet to predict 3D clothing conditioned on human body shape and garment size parameters, and ParserNet to infer garment meshes and shape under clothing with personal details in a single pass from an input mesh. SizerNet allows to estimate and visualize the dressing effect of a garment in various sizes, and ParserNet allows to edit clothing of an input mesh directly, removing the need for scan segmentation, which is a challenging problem in itself. To learn these models, we introduce the SIZER dataset of clothing size variation which includes $100$ different subjects wearing casual clothing items in various sizes, totaling to approximately 2000 scans. This dataset includes the scans, registrations to the SMPL model, scans segmented in clothing parts, garment category and size labels. Our experiments show better parsing accuracy and size prediction than baseline methods trained on SIZER. The code, model and dataset will be released for research purposes.
