  Neural implicit surface representations have emerged as a promising paradigm
to capture 3D shapes in a continuous and resolution-independent manner.
However, adapting them to articulated shapes is non-trivial. Existing
approaches learn a backward warp field that maps deformed to canonical points.
However, this is problematic since the backward warp field is pose dependent
and thus requires large amounts of data to learn. To address this, we introduce
SNARF, which combines the advantages of linear blend skinning (LBS) for
polygonal meshes with those of neural implicit surfaces by learning a forward
deformation field without direct supervision. This deformation field is defined
in canonical, pose-independent space, allowing for generalization to unseen
poses. Learning the deformation field from posed meshes alone is challenging
since the correspondences of deformed points are defined implicitly and may not
be unique under changes of topology. We propose a forward skinning model that
finds all canonical correspondences of any deformed point using iterative root
finding. We derive analytical gradients via implicit differentiation, enabling
end-to-end training from 3D meshes with bone transformations. Compared to
state-of-the-art neural implicit representations, our approach generalizes
better to unseen poses while preserving accuracy. We demonstrate our method in
challenging scenarios on (clothed) 3D humans in diverse and unseen poses.
