We present a learning‐based approach for virtual try‐on applications based on a fully convolutional graph neural network. In contrast to existing data‐driven models, which are trained for a specific garment or mesh topology, our fully convolutional model can cope with a large family of garments, represented as parametric predefined 2D panels with arbitrary mesh topology, including long dresses, shirts, and tight tops. Under the hood, our novel geometric deep learning approach learns to drape 3D garments by decoupling the three different sources of deformations that condition the fit of clothing: garment type, target body shape, and material. Specifically, we first learn a regressor that predicts the 3D drape of the input parametric garment when worn by a mean body shape. Then, after a mesh topology optimization step where we generate a sufficient level of detail for the input garment type, we further deform the mesh to reproduce deformations caused by the target body shape. Finally, we predict fine‐scale details such as wrinkles that depend mostly on the garment material. We qualitatively and quantitatively demonstrate that our fully convolutional approach outperforms existing methods in terms of generalization capabilities and memory requirements, and therefore it opens the door to more general learning‐based models for virtual try‐on applications.

中文翻译：

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用于参数化虚拟试穿的全卷积图神经网络

我们为基于完全卷积图神经网络的虚拟试穿应用程序提出了一种基于学习的方法。与针对特定服装或网格拓扑训练的现有数据驱动模型相比，我们的完全卷积模型可以处理大量服装，表示为具有任意网格拓扑的参数化预定义 2D 面板，包括长裙、衬衫、和紧身衣。在幕后，我们新颖的几何深度学习方法通​​过解耦影响服装合身性的三种不同变形来源：服装类型、目标体型和材料来学习悬垂 3D 服装。具体来说，我们首先学习一个回归器，该回归器预测输入参数服装在穿着平均体型时的 3D 悬垂性。然后，在我们为输入的服装类型生成足够详细程度的网格拓扑优化步骤之后，我们进一步变形网格以再现由目标身体形状引起的变形。最后，我们预测主要取决于服装材料的精细细节，例如皱纹。我们定性和定量地证明，我们的完全卷积方法在泛化能力和内存要求方面优于现有方法，因此它为虚拟试穿应用程序打开了更通用的基于学习的模型的大门。