  Visually exploring in a real-world 4D spatiotemporal space freely in VR has
been a long-term quest. The task is especially appealing when only a few or
even single RGB cameras are used for capturing the dynamic scene. To this end,
we present an efficient framework capable of fast reconstruction, compact
modeling, and streamable rendering. First, we propose to decompose the 4D
spatiotemporal space according to temporal characteristics. Points in the 4D
space are associated with probabilities of belonging to three categories:
static, deforming, and new areas. Each area is represented and regularized by a
separate neural field. Second, we propose a hybrid representations based
feature streaming scheme for efficiently modeling the neural fields. Our
approach, coined NeRFPlayer, is evaluated on dynamic scenes captured by single
hand-held cameras and multi-camera arrays, achieving comparable or superior
rendering performance in terms of quality and speed comparable to recent
state-of-the-art methods, achieving reconstruction in 10 seconds per frame and
interactive rendering.
