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arXiv 2201.13063cs.CVcs.AIcs.GR
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NeuralTailor：从服装三维点云重建缝纫花样结构

NeuralTailor: Reconstructing Sewing Pattern Structures from 3D Point Clouds of Garments

KAIST（韩国科学技术院）

Maria Korosteleva, Sung-Hee Lee

备注：
Updated to the version accepted to SIGGRAPH 2022 (Journal Track)
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摘要：

SocialVR、性能捕捉和虚拟试穿等领域通常需要在虚拟世界中忠实地再现真实服装。一个关键的任务是从由于织物特性、物理力和与身体接触而引起的变形中解开固有的服装形状。我们建议使用一个服装缝纫模式，一个现实的和紧凑的服装描述符，以促进内在的服装形状估计。另一个主要挑战是该领域的形状和设计的高度多样性。在3D服装上进行深度学习的最常见方法是为单个服装或服装类型构建专门的模型。我们认为，建立一个统一的模型，为各种服装设计的好处，推广到新的服装类型，因此覆盖了更大的设计域比个别模型。我们介绍了NeuralTailor，一种新的体系结构的基础上点级的注意力与可变基数集回归，并将其应用到重建2D服装缝纫模式的任务，从3D点可以服装模型。我们的实验表明，NeuralTailor成功地重建了缝纫模式，并推广到训练过程中看不到的模式拓扑的服装类型。

英文摘要：

The fields of SocialVR, performance capture, and virtual try-on are often faced with a need to faithfully reproduce real garments in the virtual world. One critical task is the disentanglement of the intrinsic garment shape from deformations due to fabric properties, physical forces, and contact with the body. We propose to use a garment sewing pattern, a realistic and compact garment descriptor, to facilitate the intrinsic garment shape estimation. Another major challenge is a high diversity of shapes and designs in the domain. The most common approach for Deep Learning on 3D garments is to build specialized models for individual garments or garment types. We argue that building a unified model for various garment designs has the benefit of generalization to novel garment types, hence covering a larger design domain than individual models would. We introduce NeuralTailor, a novel architecture based on point-level attention for set regression with variable cardinality, and apply it to the task of reconstructing 2D garment sewing patterns from the 3D point could garment models. Our experiments show that NeuralTailor successfully reconstructs sewing patterns and generalizes to garment types with pattern topologies unseen during training.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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