  Neural Radiance Field (NeRF) excels in photo-realistically static scenes,
inspiring numerous efforts to facilitate volumetric videos. However, rendering
dynamic and long-sequence radiance fields remains challenging due to the
significant data required to represent volumetric videos. In this paper, we
propose a novel end-to-end joint optimization scheme of dynamic NeRF
representation and compression, called JointRF, thus achieving significantly
improved quality and compression efficiency against the previous methods.
Specifically, JointRF employs a compact residual feature grid and a coefficient
feature grid to represent the dynamic NeRF. This representation handles large
motions without compromising quality while concurrently diminishing temporal
redundancy. We also introduce a sequential feature compression subnetwork to
further reduce spatial-temporal redundancy. Finally, the representation and
compression subnetworks are end-to-end trained combined within the JointRF.
Extensive experiments demonstrate that JointRF can achieve superior compression
performance across various datasets.
