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

Semantic Attention Flow Fields for Monocular Dynamic Scene Decomposition