Item

Abstract

3D reconstruction and novel view synthesis of dynamic scenes from collections
of single views recently gained increased attention. Existing work shows
impressive results for synthetic setups and forward-facing real-world data, but
is severely 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} 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 acceleration at training and
inference time; and 2) A static module representing the canonical scene as a
fast hash-encoded neural radiance field. We evaluate the proposed approach on
the established synthetic D-NeRF benchmark, that enables efficient
reconstruction from a single monocular view per time-frame randomly sampled
from a full hemisphere. We refer to this form of inputs as monocularized data.
To prove its practicality for real-world scenarios, we recorded twelve
challenging sequences with human actors by sampling single frames from a
synchronized multi-view rig. In both cases, our method is trained significantly
faster than previous methods (minutes instead of days) while achieving 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.