  From video, we reconstruct a neural volume that captures time-varying color,
density, scene flow, semantics, and attention information. The semantics and
attention let us identify salient foreground objects separately from the
background across spacetime. To mitigate low resolution semantic and attention
features, we compute pyramids that trade detail with whole-image context. After
optimization, we perform a saliency-aware clustering to decompose the scene. To
evaluate real-world scenes, we annotate object masks in the NVIDIA Dynamic
Scene and DyCheck datasets. We demonstrate that this method can decompose
dynamic scenes in an unsupervised way with competitive performance to a
supervised method, and that it improves foreground/background segmentation over
recent static/dynamic split methods. Project Webpage:
https://visual.cs.brown.edu/saff
