  3D Clothing modeling and datasets play crucial role in the entertainment,
animation, and digital fashion industries. Existing work often lacks detailed
semantic understanding or uses synthetic datasets, lacking realism and
personalization. To address this, we first introduce CloSe-D: a novel
large-scale dataset containing 3D clothing segmentation of 3167 scans, covering
a range of 18 distinct clothing classes. Additionally, we propose CloSe-Net,
the first learning-based 3D clothing segmentation model for fine-grained
segmentation from colored point clouds. CloSe-Net uses local point features,
body-clothing correlation, and a garment-class and point features-based
attention module, improving performance over baselines and prior work. The
proposed attention module enables our model to learn appearance and
geometry-dependent clothing prior from data. We further validate the efficacy
of our approach by successfully segmenting publicly available datasets of
people in clothing. We also introduce CloSe-T, a 3D interactive tool for
refining segmentation labels. Combining the tool with CloSe-T in a continual
learning setup demonstrates improved generalization on real-world data.
Dataset, model, and tool can be found at
https://virtualhumans.mpi-inf.mpg.de/close3dv24/.
