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.

翻译：本文提出 Neural-ABC，一种基于神经隐式函数的新型参数化模型，能够以解耦的身份、衣物、体型与姿态隐空间表征穿衣人体。传统基于网格的表示方法因人体体型多样性、服装样式复杂性及姿态变化而难以处理穿衣人体建模。本模型为参数化建模提供统一框架，可同时表征穿衣人体的身份、衣物、体型与姿态。该方法利用神经隐式函数的表征能力，集成精心设计的结构以满足必要需求。具体而言，我们以符号距离函数表示底层人体，以无符号距离函数表示衣物，二者可统一表示为无符号距离场。不同衣物无需预定义拓扑结构或分类，可随底层人体变化而贴合身体。此外，我们采用可控关节结构构建姿态。模型在公开数据集及新建数据集上训练，并精心设计解耦策略以确保最优性能。该模型擅长在不同体型与姿态下解耦衣物与身份，同时保留衣物风格。我们证明 Neural-ABC 可拟合不同类型衣物的新观测数据。相较于其他最先进参数化模型，Neural-ABC 在穿衣人体重建中表现出显著优势（通过拟合原始扫描数据、深度图及图像验证）。拟合结果的属性可通过调整身份、衣物、体型与姿态编码进一步编辑。