  We present ClothCombo, a pipeline to drape arbitrary combinations of clothes
on 3D human models with varying body shapes and poses. While existing
learning-based approaches for draping clothes have shown promising results,
multi-layered clothing remains challenging as it is non-trivial to model
inter-cloth interaction. To this end, our method utilizes a GNN-based network
to efficiently model the interaction between clothes in different layers, thus
enabling multi-layered clothing. Specifically, we first create feature
embedding for each cloth using a topology-agnostic network. Then, the draping
network deforms all clothes to fit the target body shape and pose without
considering inter-cloth interaction. Lastly, the untangling network predicts
the per-vertex displacements in a way that resolves interpenetration between
clothes. In experiments, the proposed model demonstrates strong performance in
complex multi-layered scenarios. Being agnostic to cloth topology, our method
can be readily used for layered virtual try-on of real clothes in diverse poses
and combinations of clothes.
