  This paper introduces Motion-oriented Compositional Neural Radiance Fields
(MoCo-NeRF), a framework designed to perform free-viewpoint rendering of
monocular human videos via novel non-rigid motion modeling approach. In the
context of dynamic clothed humans, complex cloth dynamics generate non-rigid
motions that are intrinsically distinct from skeletal articulations and
critically important for the rendering quality. The conventional approach
models non-rigid motions as spatial (3D) deviations in addition to skeletal
transformations. However, it is either time-consuming or challenging to achieve
optimal quality due to its high learning complexity without a direct
supervision. To target this problem, we propose a novel approach of modeling
non-rigid motions as radiance residual fields to benefit from more direct color
supervision in the rendering and utilize the rigid radiance fields as a prior
to reduce the complexity of the learning process. Our approach utilizes a
single multiresolution hash encoding (MHE) to concurrently learn the canonical
T-pose representation from rigid skeletal motions and the radiance residual
field for non-rigid motions. Additionally, to further improve both training
efficiency and usability, we extend MoCo-NeRF to support simultaneous training
of multiple subjects within a single framework, thanks to our effective design
for modeling non-rigid motions. This scalability is achieved through the
integration of a global MHE and learnable identity codes in addition to
multiple local MHEs. We present extensive results on ZJU-MoCap and MonoCap,
clearly demonstrating state-of-the-art performance in both single- and
multi-subject settings. The code and model will be made publicly available at
the project page: https://stevejaehyeok.github.io/publications/moco-nerf.
