Each action becomes a sparse set of semantic action elements with continuous spatial and temporal coordinates. The model maps those coordinates into a pretrained video model's positional space and uses cardinality-calibrated attention to balance action representations with different element counts.
UniAction helps researchers study compositional action control and generalization across scenes and timing. Its shared representation makes it possible to compare multiple control types within one pipeline rather than maintaining a completely separate conditioning mechanism for every action domain.
