In this paper, we introduce Neural-ABC, a novel parametric model based on neural implicit functions that can represent clothed human bodies with disentangled latent spaces for identity, clothing, shape, and pose. Traditional mesh-based representations struggle to represent articulated bodies with clothes due to the diversity of human body shapes and clothing styles, as well as the complexity of poses. Our proposed model provides a unified framework for parametric modeling, which can represent the identity, clothing, shape and pose of the clothed human body. Our proposed approach utilizes the power of neural implicit functions as the underlying representation and integrates well-designed structures to meet the necessary requirements. Specifically, we represent the underlying body as a signed distance function and clothing as an unsigned distance function, and they can be uniformly represented as unsigned distance fields. Different types of clothing do not require predefined topological structures or classifications, and can follow changes in the underlying body to fit the body. Additionally, we construct poses using a controllable articulated structure. The model is trained on both open and newly constructed datasets, and our decoupling strategy is carefully designed to ensure optimal performance. Our model excels at disentangling clothing and identity in different shape and poses while preserving the style of the clothing. We demonstrate that Neural-ABC fits new observations of different types of clothing. Compared to other state-of-the-art parametric models, Neural-ABC demonstrates powerful advantages in the reconstruction of clothed human bodies, as evidenced by fitting raw scans, depth maps and images. We show that the attributes of the fitted results can be further edited by adjusting their identities, clothing, shape and pose codes.

中文翻译：

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Neural-ABC：带衣服的铰接体的神经参数模型

在本文中，我们介绍了 Neural-ABC，这是一种基于神经隐函数的新型参数模型，可以用解开的身份、服装、形状和姿势的潜在空间来表示穿着衣服的人体。由于人体形状和服装风格的多样性以及姿势的复杂性，传统的基于网格的表示很难用衣服来表示铰接的身体。我们提出的模型为参数化建模提供了一个统一的框架，可以表示穿着人体的身份、服装、形状和姿势。我们提出的方法利用神经隐式函数的力量作为底层表示，并集成精心设计的结构来满足必要的要求。具体来说，我们将底层身体表示为有符号距离函数，将服装表示为无符号距离函数，并且它们可以统一表示为无符号距离场。不同类型的服装不需要预先定义的拓扑结构或分类，并且可以跟随底层身体的变化来贴合身体。此外，我们使用可控的铰接结构构建姿势。该模型在开放数据集和新建数据集上进行训练，我们的解耦策略经过精心设计，以确保最佳性能。我们的模型擅长以不同的形状和姿势来区分服装和身份，同时保留服装的风格。我们证明 Neural-ABC 适合对不同类型服装的新观察。与其他最先进的参数化模型相比，Neural-ABC 在重建穿着人体时展现出强大的优势，拟合原始扫描、深度图和图像就证明了这一点。我们表明，可以通过调整其身份、服装、形状和姿势代码来进一步编辑拟合结果的属性。