Semantic-Preserved Point-based Human Avatar
Lixiang Lin Jianke Zhu
To enable realistic experience in AR/VR and digital entertainment, we present
the first point-based human avatar model that embodies the entirety expressive
range of digital humans. We employ two MLPs to model pose-dependent deformation
and linear skinning (LBS) weights. The representation of appearance relies on a
decoder and the features that attached to each point. In contrast to
alternative implicit approaches, the oriented points representation not only
provides a more intuitive way to model human avatar animation but also
significantly reduces both training and inference time. Moreover, we propose a
novel method to transfer semantic information from the SMPL-X model to the
points, which enables to better understand human body movements. By leveraging
the semantic information of points, we can facilitate virtual try-on and human
avatar composition through exchanging the points of same category across
different subjects. Experimental results demonstrate the efficacy of our
presented method.