  Deep generative models have been recently extended to synthesizing 3D digital
humans. However, previous approaches treat clothed humans as a single chunk of
geometry without considering the compositionality of clothing and accessories.
As a result, individual items cannot be naturally composed into novel
identities, leading to limited expressiveness and controllability of generative
3D avatars. While several methods attempt to address this by leveraging
synthetic data, the interaction between humans and objects is not authentic due
to the domain gap, and manual asset creation is difficult to scale for a wide
variety of objects. In this work, we present a novel framework for learning a
compositional generative model of humans and objects (backpacks, coats,
scarves, and more) from real-world 3D scans. Our compositional model is
interaction-aware, meaning the spatial relationship between humans and objects,
and the mutual shape change by physical contact is fully incorporated. The key
challenge is that, since humans and objects are in contact, their 3D scans are
merged into a single piece. To decompose them without manual annotations, we
propose to leverage two sets of 3D scans of a single person with and without
objects. Our approach learns to decompose objects and naturally compose them
back into a generative human model in an unsupervised manner. Despite our
simple setup requiring only the capture of a single subject with objects, our
experiments demonstrate the strong generalization of our model by enabling the
natural composition of objects to diverse identities in various poses and the
composition of multiple objects, which is unseen in training data.
https://taeksuu.github.io/ncho/
