Geometry Forcing: Marrying Video Diffusion and 3D Representation for
  Consistent World Modeling
Videos inherently represent 2D projections of a dynamic 3D world. However,
our analysis suggests that video diffusion models trained solely on raw video
data often fail to capture meaningful geometric-aware structure in their
learned representations. To bridge this gap between video diffusion models and
the underlying 3D nature of the physical world, we propose Geometry Forcing, a
simple yet effective method that encourages video diffusion models to
internalize latent 3D representations. Our key insight is to guide the model's
intermediate representations toward geometry-aware structure by aligning them
with features from a pretrained geometric foundation model. To this end, we
introduce two complementary alignment objectives: Angular Alignment, which
enforces directional consistency via cosine similarity, and Scale Alignment,
which preserves scale-related information by regressing unnormalized geometric
features from normalized diffusion representation. We evaluate Geometry Forcing
on both camera view-conditioned and action-conditioned video generation tasks.
Experimental results demonstrate that our method substantially improves visual
quality and 3D consistency over the baseline methods. Project page:
https://GeometryForcing.github.io.
68707ef2c8391850d6097860
Haoyu Wu
claimed_verified
2025-07-11T08:00:05.153Z
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Haoyu wu
user
Haoyuwu
68707ef2c8391850d6097861
Diankun Wu
claimed_verified
2025-07-11T16:31:56.924Z
6577fba2eb02736add6377f5
/avatars/3e486dd36021feaa4a0259cd89c1eee9.svg
Wu
user
Diankun
68707ef2c8391850d6097862
Tianyu He
claimed_verified
2025-07-11T08:00:03.023Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/619b7b1cab4c7b7f16a7d59e/6TvXaAqBghAMYO1-j5l4v.jpeg
Tianyu He
user
deeptimhe
68707ef2c8391850d6097863
Junliang Guo
68707ef2c8391850d6097864
Yang Ye
68707ef2c8391850d6097865
Yueqi Duan
68707ef2c8391850d6097866
Jiang Bian
Geometry Forcing: Marrying Video Diffusion and 3D Representation for
  Consistent World Modeling
Videos inherently represent 2D projections of a dynamic 3D world. However,
our analysis suggests that video diffusion models trained solely on raw video
data often fail to capture meaningful geometric-aware structure in their
learned representations. To bridge this gap between video diffusion models and
the underlying 3D nature of the physical world, we propose Geometry Forcing, a
simple yet effective method that encourages video diffusion models to
internalize latent 3D representations. Our key insight is to guide the model's
intermediate representations toward geometry-aware structure by aligning them
with features from a pretrained geometric foundation model. To this end, we
introduce two complementary alignment objectives: Angular Alignment, which
enforces directional consistency via cosine similarity, and Scale Alignment,
which preserves scale-related information by regressing unnormalized geometric
features from normalized diffusion representation. We evaluate Geometry Forcing
on both camera view-conditioned and action-conditioned video generation tasks.
Experimental results demonstrate that our method substantially improves visual
quality and 3D consistency over the baseline methods. Project page:
https://GeometryForcing.github.io.
Geometry Forcing enhances video diffusion models by aligning their intermediate representations with geometric features from a pretrained model, improving visual quality and 3D consistency.
video diffusion models latent 3D representations pretrained geometric foundation model Angular Alignment Scale Alignment camera view-conditioned action-conditioned video generation tasks