  Designing a 3D representation of a dynamic scene for fast optimization and
rendering is a challenging task. While recent explicit representations enable
fast learning and rendering of dynamic radiance fields, they require a dense
set of input viewpoints. In this work, we focus on learning a fast
representation for dynamic radiance fields with sparse input viewpoints.
However, the optimization with sparse input is under-constrained and
necessitates the use of motion priors to constrain the learning. Existing fast
dynamic scene models do not explicitly model the motion, making them difficult
to be constrained with motion priors. We design an explicit motion model as a
factorized 4D representation that is fast and can exploit the spatio-temporal
correlation of the motion field. We then introduce reliable flow priors
including a combination of sparse flow priors across cameras and dense flow
priors within cameras to regularize our motion model. Our model is fast,
compact and achieves very good performance on popular multi-view dynamic scene
datasets with sparse input viewpoints. The source code for our model can be
found on our project page:
https://nagabhushansn95.github.io/publications/2024/RF-DeRF.html.
