# Temporal Transfer Layer

The work proposes TT-VidT, which pairs a DINOv3-initialized ViT-B/16 per-frame spatial path with a compact Temporal Transfer Layer and trains it by Diff Compression to reconstruct target frames from a first-frame appearance anchor and frame-specific motion tokens; under a matched recipe at roughly 170M-190M encoder scale on 1.7M OpenVid and Moments-in-Time v2 clips for 8 epochs, a 24-cell architecture-objective sweep shows that only TT3D with Diff Compression enters the strongest motion-sensitive regime, and in the final comparison it leads Jester, Something-Something V2, ARID and Diving48 fine-tuning simultaneously while using 48% fewer encoder FLOPs than DisMo and 55% fewer than VideoMAE or V-JEPA 2.

The work proposes TT-VidT, which pairs a DINOv3-initialized ViT-B/16 per-frame spatial path with a compact Temporal Transfer Layer and trains it by Diff Compression to reconstruct target frames from a first-frame appearance anchor and frame-specific motion tokens; under a matched recipe at roughly 170M-190M encoder scale on 1.7M OpenVid and Moments-in-Time v2 clips for 8 epochs, a 24-cell architecture-objective sweep shows that only TT3D with Diff Compression enters the strongest motion-sensitive regime, and in the final comparison it leads Jester, Something-Something V2, ARID and Diving48 fine-tuning simultaneously while using 48% fewer encoder FLOPs than DisMo and 55% fewer than VideoMAE or V-JEPA 2.