  We present a novel dense semantic forecasting approach which is applicable to
a variety of architectures and tasks. The approach consists of two modules.
Feature-to-motion (F2M) module forecasts a dense deformation field which warps
past features into their future positions. Feature-to-feature (F2F) module
regresses the future features directly and is therefore able to account for
emergent scenery. The compound F2MF approach decouples effects of motion from
the effects of novelty in a task-agnostic manner. We aim to apply F2MF
forecasting to the most subsampled and the most abstract representation of a
desired single-frame model. Our implementations take advantage of deformable
convolutions and pairwise correlation coefficients across neighbouring time
instants. We perform experiments on three dense prediction tasks: semantic
segmentation, instance-level segmentation, and panoptic segmentation. The
results reveal state-of-the-art forecasting accuracy across all three
modalities on the Cityscapes dataset.
