  We present Neural Generalized Implicit Functions(Neural-GIF), to animate
people in clothing as a function of the body pose. Given a sequence of scans of
a subject in various poses, we learn to animate the character for new poses.
Existing methods have relied on template-based representations of the human
body (or clothing). However such models usually have fixed and limited
resolutions, require difficult data pre-processing steps and cannot be used
with complex clothing. We draw inspiration from template-based methods, which
factorize motion into articulation and non-rigid deformation, but generalize
this concept for implicit shape learning to obtain a more flexible model. We
learn to map every point in the space to a canonical space, where a learned
deformation field is applied to model non-rigid effects, before evaluating the
signed distance field. Our formulation allows the learning of complex and
non-rigid deformations of clothing and soft tissue, without computing a
template registration as it is common with current approaches. Neural-GIF can
be trained on raw 3D scans and reconstructs detailed complex surface geometry
and deformations. Moreover, the model can generalize to new poses. We evaluate
our method on a variety of characters from different public datasets in diverse
clothing styles and show significant improvements over baseline methods,
quantitatively and qualitatively. We also extend our model to multiple shape
setting. To stimulate further research, we will make the model, code and data
publicly available at: https://virtualhumans.mpi-inf.mpg.de/neuralgif/
