  Modeling dynamic scenes is important for many applications such as virtual
reality and telepresence. Despite achieving unprecedented fidelity for novel
view synthesis in dynamic scenes, existing methods based on Neural Radiance
Fields (NeRF) suffer from slow convergence (i.e., model training time measured
in days). In this paper, we present DeVRF, a novel representation to accelerate
learning dynamic radiance fields. The core of DeVRF is to model both the 3D
canonical space and 4D deformation field of a dynamic, non-rigid scene with
explicit and discrete voxel-based representations. However, it is quite
challenging to train such a representation which has a large number of model
parameters, often resulting in overfitting issues. To overcome this challenge,
we devise a novel static-to-dynamic learning paradigm together with a new data
capture setup that is convenient to deploy in practice. This paradigm unlocks
efficient learning of deformable radiance fields via utilizing the 3D
volumetric canonical space learnt from multi-view static images to ease the
learning of 4D voxel deformation field with only few-view dynamic sequences. To
further improve the efficiency of our DeVRF and its synthesized novel view's
quality, we conduct thorough explorations and identify a set of strategies. We
evaluate DeVRF on both synthetic and real-world dynamic scenes with different
types of deformation. Experiments demonstrate that DeVRF achieves two orders of
magnitude speedup (100x faster) with on-par high-fidelity results compared to
the previous state-of-the-art approaches. The code and dataset will be released
in https://github.com/showlab/DeVRF.
