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

Animating NeRFs from Texture Space: A Framework for Pose-Dependent Rendering of Human Performances