Snapshot-to-Flow: Residual Dynamics for Adapting Pretrained 4D Scenes

Feed-forward 4D reconstruction supplies a reusable scene prior, but individual videos retain motion and appearance errors. We introduce Snapshot-to-Flow, a scene-level adapter that learns residual dynamics around pretrained trajectories. A shared velocity field integrates source-referenced position residuals; local appearance and scene-wide color controls address complementary rendering errors before an unchanged rasterizer. Complete sequence records with dynamic-branch updates show mean novel-frame PSNR gains of 1.29, 2.25 and 1.12 dB on STAG4D, HyperNeRF and Neural 3D Video. These validation gains retain all evaluated sequences, including negative-gain cases. An eight-scene motion-query study reports a mean gain of 1.31 dB beyond the conditioning interval. Component and scene-attribute diagnostics characterize the complete adaptation procedure; separate cross-scene controls distinguish its benefits from integration-specific superiority.