  We present FlexNeRF, a method for photorealistic freeviewpoint rendering of
humans in motion from monocular videos. Our approach works well with sparse
views, which is a challenging scenario when the subject is exhibiting
fast/complex motions. We propose a novel approach which jointly optimizes a
canonical time and pose configuration, with a pose-dependent motion field and
pose-independent temporal deformations complementing each other. Thanks to our
novel temporal and cyclic consistency constraints along with additional losses
on intermediate representation such as segmentation, our approach provides high
quality outputs as the observed views become sparser. We empirically
demonstrate that our method significantly outperforms the state-of-the-art on
public benchmark datasets as well as a self-captured fashion dataset. The
project page is available at: https://flex-nerf.github.io/
