  We focus on reconstructing high-fidelity radiance fields of human heads,
capturing their animations over time, and synthesizing re-renderings from novel
viewpoints at arbitrary time steps. To this end, we propose a new multi-view
capture setup composed of 16 calibrated machine vision cameras that record
time-synchronized images at 7.1 MP resolution and 73 frames per second. With
our setup, we collect a new dataset of over 4700 high-resolution,
high-framerate sequences of more than 220 human heads, from which we introduce
a new human head reconstruction benchmark. The recorded sequences cover a wide
range of facial dynamics, including head motions, natural expressions,
emotions, and spoken language. In order to reconstruct high-fidelity human
heads, we propose Dynamic Neural Radiance Fields using Hash Ensembles
(NeRSemble). We represent scene dynamics by combining a deformation field and
an ensemble of 3D multi-resolution hash encodings. The deformation field allows
for precise modeling of simple scene movements, while the ensemble of hash
encodings helps to represent complex dynamics. As a result, we obtain radiance
field representations of human heads that capture motion over time and
facilitate re-rendering of arbitrary novel viewpoints. In a series of
experiments, we explore the design choices of our method and demonstrate that
our approach outperforms state-of-the-art dynamic radiance field approaches by
a significant margin.
