# GAD (Geometry Attention Distillation)

Video diffusion models lose scene geometry when the camera moves or objects move, because cross-frame attention often spreads its response over several regions instead of locking onto the same scene element. GAD extracts correspondence targets from the attention of a frozen geometry foundation model, VGGT-, on clean videos, and routes supervision by noise timestep to the layer where scene structure is forming, training video attention with pairwise and trackwise objectives. On 15-second long-range generation with RealEstate10K and DL3DV-10K, adding GAD to Wan2.2 5B raises cross-frame matching hit rate by at least 30% relative on both datasets, with no extra inference modules or computation.

Video diffusion models lose scene geometry when the camera moves or objects move, because cross-frame attention often spreads its response over several regions instead of locking onto the same scene element. GAD extracts correspondence targets from the attention of a frozen geometry foundation model, VGGT-, on clean videos, and routes supervision by noise timestep to the layer where scene structure is forming, training video attention with pairwise and trackwise objectives. On 15-second long-range generation with RealEstate10K and DL3DV-10K, adding GAD to Wan2.2 5B raises cross-frame matching hit rate by at least 30% relative on both datasets, with no extra inference modules or computation.