  Creating high-quality controllable 3D human models from multi-view RGB videos
poses a significant challenge. Neural radiance fields (NeRFs) have demonstrated
remarkable quality in reconstructing and free-viewpoint rendering of static as
well as dynamic scenes. The extension to a controllable synthesis of dynamic
human performances poses an exciting research question. In this paper, we
introduce a novel NeRF-based framework for pose-dependent rendering of human
performances. In our approach, the radiance field is warped around an SMPL body
mesh, thereby creating a new surface-aligned representation. Our representation
can be animated through skeletal joint parameters that are provided to the NeRF
in addition to the viewpoint for pose dependent appearances. To achieve this,
our representation includes the corresponding 2D UV coordinates on the mesh
texture map and the distance between the query point and the mesh. To enable
efficient learning despite mapping ambiguities and random visual variations, we
introduce a novel remapping process that refines the mapped coordinates.
Experiments demonstrate that our approach results in high-quality renderings
for novel-view and novel-pose synthesis.
