  The reconstruction and novel view synthesis of dynamic scenes recently gained
increased attention. As reconstruction from large-scale multi-view data
involves immense memory and computational requirements, recent benchmark
datasets provide collections of single monocular views per timestamp sampled
from multiple (virtual) cameras. We refer to this form of inputs as
"monocularized" data. Existing work shows impressive results for synthetic
setups and forward-facing real-world data, but is often limited in the training
speed and angular range for generating novel views. This paper addresses these
limitations and proposes a new method for full 360{\deg} inward-facing novel
view synthesis of non-rigidly deforming scenes. At the core of our method are:
1) An efficient deformation module that decouples the processing of spatial and
temporal information for accelerated training and inference; and 2) A static
module representing the canonical scene as a fast hash-encoded neural radiance
field. In addition to existing synthetic monocularized data, we systematically
analyze the performance on real-world inward-facing scenes using a newly
recorded challenging dataset sampled from a synchronized large-scale multi-view
rig. In both cases, our method is significantly faster than previous methods,
converging in less than 7 minutes and achieving real-time framerates at 1K
resolution, while obtaining a higher visual accuracy for generated novel views.
Our source code and data is available at our project page
https://graphics.tu-bs.de/publications/kappel2022fast.
