  We present SCANimate, an end-to-end trainable framework that takes raw 3D
scans of a clothed human and turns them into an animatable avatar. These
avatars are driven by pose parameters and have realistic clothing that moves
and deforms naturally. SCANimate does not rely on a customized mesh template or
surface mesh registration. We observe that fitting a parametric 3D body model,
like SMPL, to a clothed human scan is tractable while surface registration of
the body topology to the scan is often not, because clothing can deviate
significantly from the body shape. We also observe that articulated
transformations are invertible, resulting in geometric cycle consistency in the
posed and unposed shapes. These observations lead us to a weakly supervised
learning method that aligns scans into a canonical pose by disentangling
articulated deformations without template-based surface registration.
Furthermore, to complete missing regions in the aligned scans while modeling
pose-dependent deformations, we introduce a locally pose-aware implicit
function that learns to complete and model geometry with learned pose
correctives. In contrast to commonly used global pose embeddings, our local
pose conditioning significantly reduces long-range spurious correlations and
improves generalization to unseen poses, especially when training data is
limited. Our method can be applied to pose-aware appearance modeling to
generate a fully textured avatar. We demonstrate our approach on various
clothing types with different amounts of training data, outperforming existing
solutions and other variants in terms of fidelity and generality in every
setting. The code is available at https://scanimate.is.tue.mpg.de.
