UniGeo: Taming Video Diffusion for Unified Consistent Geometry
  Estimation
Recently, methods leveraging diffusion model priors to assist monocular
geometric estimation (e.g., depth and normal) have gained significant attention
due to their strong generalization ability. However, most existing works focus
on estimating geometric properties within the camera coordinate system of
individual video frames, neglecting the inherent ability of diffusion models to
determine inter-frame correspondence. In this work, we demonstrate that,
through appropriate design and fine-tuning, the intrinsic consistency of video
generation models can be effectively harnessed for consistent geometric
estimation. Specifically, we 1) select geometric attributes in the global
coordinate system that share the same correspondence with video frames as the
prediction targets, 2) introduce a novel and efficient conditioning method by
reusing positional encodings, and 3) enhance performance through joint training
on multiple geometric attributes that share the same correspondence. Our
results achieve superior performance in predicting global geometric attributes
in videos and can be directly applied to reconstruction tasks. Even when
trained solely on static video data, our approach exhibits the potential to
generalize to dynamic video scenes.
683d11d1495f0b58f2fd49a9
Yang-Tian Sun
683d11d1495f0b58f2fd49aa
Xin Yu
683d11d1495f0b58f2fd49ab
Zehuan Huang
683d11d1495f0b58f2fd49ac
Yi-Hua Huang
683d11d1495f0b58f2fd49ad
Yuan-Chen Guo
683d11d1495f0b58f2fd49ae
Ziyi Yang
683d11d1495f0b58f2fd49af
Yan-Pei Cao
683d11d1495f0b58f2fd49b0
Xiaojuan Qi
claimed_verified
2025-07-15T19:12:09.978Z
6875266f9cd3191dfddc7071
/avatars/64c581910833b111e9a7bae5b8740229.svg
xiaojuan qi
user
xjqi
UniGeo: Taming Video Diffusion for Unified Consistent Geometry
  Estimation
Recently, methods leveraging diffusion model priors to assist monocular
geometric estimation (e.g., depth and normal) have gained significant attention
due to their strong generalization ability. However, most existing works focus
on estimating geometric properties within the camera coordinate system of
individual video frames, neglecting the inherent ability of diffusion models to
determine inter-frame correspondence. In this work, we demonstrate that,
through appropriate design and fine-tuning, the intrinsic consistency of video
generation models can be effectively harnessed for consistent geometric
estimation. Specifically, we 1) select geometric attributes in the global
coordinate system that share the same correspondence with video frames as the
prediction targets, 2) introduce a novel and efficient conditioning method by
reusing positional encodings, and 3) enhance performance through joint training
on multiple geometric attributes that share the same correspondence. Our
results achieve superior performance in predicting global geometric attributes
in videos and can be directly applied to reconstruction tasks. Even when
trained solely on static video data, our approach exhibits the potential to
generalize to dynamic video scenes.
Video generation models leveraging diffusion priors achieve superior global geometric attribute estimation and reconstructions, benefiting from inter-frame consistency and joint training on shared attributes.
diffusion models monocular geometric estimation depth normal camera coordinate system intrinsic consistency video generation models global coordinate system positional encodings joint training static video data dynamic video scenes