  Combining human body models with differentiable rendering has recently
enabled animatable avatars of clothed humans from sparse sets of multi-view RGB
videos. While state-of-the-art approaches achieve realistic appearance with
neural radiance fields (NeRF), the inferred geometry often lacks detail due to
missing geometric constraints. Further, animating avatars in
out-of-distribution poses is not yet possible because the mapping from
observation space to canonical space does not generalize faithfully to unseen
poses. In this work, we address these shortcomings and propose a model to
create animatable clothed human avatars with detailed geometry that generalize
well to out-of-distribution poses. To achieve detailed geometry, we combine an
articulated implicit surface representation with volume rendering. For
generalization, we propose a novel joint root-finding algorithm for
simultaneous ray-surface intersection search and correspondence search. Our
algorithm enables efficient point sampling and accurate point canonicalization
while generalizing well to unseen poses. We demonstrate that our proposed
pipeline can generate clothed avatars with high-quality pose-dependent geometry
and appearance from a sparse set of multi-view RGB videos. Our method achieves
state-of-the-art performance on geometry and appearance reconstruction while
creating animatable avatars that generalize well to out-of-distribution poses
beyond the small number of training poses.
