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.
翻译:SocialVR、性能捕捉和虚拟试镜等领域往往面临在虚拟世界忠实复制真实服装的需要。 一项关键任务是将内在服装形状与因织物特性、物理力和与身体的接触而产生的变形脱钩脱钩。 我们提议使用服装缝纫模式,即一个现实和紧凑的服装描述符,以便利对内在服装形状的估测。 另一个重大挑战是域内的形状和设计的多样性。 3D服装深造最常见的方法是为个人服装或服装类型建立专门模型。 我们认为,为各种服装设计建立一个统一的模型,其好处是通用化为新型服装类型,从而涵盖比单个模型更大的设计领域。 我们引入了NeuralTailor, 这是一种基于点关注的新型结构, 将回归与变异的基点联系起来, 并将其应用于从3D点重建2D服装缝纫模式的任务中可以制成模型。 我们的实验显示, NeoralTailor成功地重建缝纫模式, 以及将服装类型转化为在培训过程中看不见的形态表层。