  In this work, we pioneer Semantic Flow, a neural semantic representation of
dynamic scenes from monocular videos. In contrast to previous NeRF methods that
reconstruct dynamic scenes from the colors and volume densities of individual
points, Semantic Flow learns semantics from continuous flows that contain rich
3D motion information. As there is 2D-to-3D ambiguity problem in the viewing
direction when extracting 3D flow features from 2D video frames, we consider
the volume densities as opacity priors that describe the contributions of flow
features to the semantics on the frames. More specifically, we first learn a
flow network to predict flows in the dynamic scene, and propose a flow feature
aggregation module to extract flow features from video frames. Then, we propose
a flow attention module to extract motion information from flow features, which
is followed by a semantic network to output semantic logits of flows. We
integrate the logits with volume densities in the viewing direction to
supervise the flow features with semantic labels on video frames. Experimental
results show that our model is able to learn from multiple dynamic scenes and
supports a series of new tasks such as instance-level scene editing, semantic
completions, dynamic scene tracking and semantic adaption on novel scenes.
Codes are available at https://github.com/tianfr/Semantic-Flow/.
