  Neural fields such as implicit surfaces have recently enabled avatar modeling
from raw scans without explicit temporal correspondences. In this work, we
exploit autoregressive modeling to further extend this notion to capture
dynamic effects, such as soft-tissue deformations. Although autoregressive
models are naturally capable of handling dynamics, it is non-trivial to apply
them to implicit representations, as explicit state decoding is infeasible due
to prohibitive memory requirements. In this work, for the first time, we enable
autoregressive modeling of implicit avatars. To reduce the memory bottleneck
and efficiently model dynamic implicit surfaces, we introduce the notion of
articulated observer points, which relate implicit states to the explicit
surface of a parametric human body model. We demonstrate that encoding implicit
surfaces as a set of height fields defined on articulated observer points leads
to significantly better generalization compared to a latent representation. The
experiments show that our approach outperforms the state of the art, achieving
plausible dynamic deformations even for unseen motions.
https://zqbai-jeremy.github.io/autoavatar
