  We propose Neural Deformable Fields (NDF), a new representation for dynamic
human digitization from a multi-view video. Recent works proposed to represent
a dynamic human body with shared canonical neural radiance fields which links
to the observation space with deformation fields estimations. However, the
learned canonical representation is static and the current design of the
deformation fields is not able to represent large movements or detailed
geometry changes. In this paper, we propose to learn a neural deformable field
wrapped around a fitted parametric body model to represent the dynamic human.
The NDF is spatially aligned by the underlying reference surface. A neural
network is then learned to map pose to the dynamics of NDF. The proposed NDF
representation can synthesize the digitized performer with novel views and
novel poses with a detailed and reasonable dynamic appearance. Experiments show
that our method significantly outperforms recent human synthesis methods.
