The authors use VGGT-Ω to extract visual tokens associated with estimated 3D points and confidence scores.
Routing 3D geometry into diffusion improves sparse-view scene synthesis
VGGT-Diff uses confidence-weighted 3D evidence to guide a video diffusion model and stabilize jointly generated viewpoints.
Top University
Kangjie Chen · Xiangyu Li · Dongbin Zhang · Chaoda Zheng · Shijia Chen · Jinhao Deng · +9 more
XPeng Motors · The Chinese University of Hong Kong · Tsinghua University
Research Digest··2 min read
Chen and colleagues combine an explicit visual-geometry model with a pretrained video diffusion model to synthesize novel views from sparse images.
Why this paper
From The Chinese University of Hong Kong and 2 others
In one line
VGGT-Diff achieves state-of-the-art novel view synthesis from sparse views by routing visual geometry latents from VGGT-Ω into a pretrained video diffusion model.
What we could check
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