# Multi-View Diffusion

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.

UltraTex is an end-to-end multi-view diffusion framework that scales image-guided 3D texturing from 512/768 to 2048 resolution through Background Token Dropping, Block-Sparse Attention, and Foreground-Aware VAE Decoding, and it builds G-buffer TexVerse covering over 268,000 3D assets, achieving 20.6-91.1x training speedup and 22.3-74.6x end-to-end inference speedup within the dataset's common foreground-ratio range of 10%-30%.

UltraTex is an end-to-end multi-view diffusion framework that scales image-guided 3D texturing from 512/768 to 2048 resolution through Background Token Dropping, Block-Sparse Attention, and Foreground-Aware VAE Decoding, and it builds G-buffer TexVerse covering over 268,000 3D assets, achieving 20.6-91.1x training speedup and 22.3-74.6x end-to-end inference speedup within the dataset's common foreground-ratio range of 10%-30%.