  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.
