# VGGT

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.

VGGT-Diff is a geometry-routed multi-view diffusion framework that uses a frozen VGGT-Ω to transform source-view features together with 3D points and confidence into query-aligned conditions via a confidence-aware Visual Geometry Router, injects them into a pretrained Wan2.1-I2V-14B video diffusion model, and regularizes predicted-clean residuals along reliable 3D tracks with Point-Track Residual Consistency; trained on only 980 DL3DV scenes, it ranks first in PSNR, LPIPS and DreamSim and second in SSIM on 6,188 DL3DV-Benchmark targets, and first in LPIPS and DreamSim with second-place PSNR on zero-shot Mip-NeRF 360.

VGGT-Diff is a geometry-routed multi-view diffusion framework that uses a frozen VGGT-Ω to transform source-view features together with 3D points and confidence into query-aligned conditions via a confidence-aware Visual Geometry Router, injects them into a pretrained Wan2.1-I2V-14B video diffusion model, and regularizes predicted-clean residuals along reliable 3D tracks with Point-Track Residual Consistency; trained on only 980 DL3DV scenes, it ranks first in PSNR, LPIPS and DreamSim and second in SSIM on 6,188 DL3DV-Benchmark targets, and first in LPIPS and DreamSim with second-place PSNR on zero-shot Mip-NeRF 360.