FlowMo: Variance-Based Flow Guidance for Coherent Motion in Video
  Generation
Text-to-video diffusion models are notoriously limited in their ability to
model temporal aspects such as motion, physics, and dynamic interactions.
Existing approaches address this limitation by retraining the model or
introducing external conditioning signals to enforce temporal consistency. In
this work, we explore whether a meaningful temporal representation can be
extracted directly from the predictions of a pre-trained model without any
additional training or auxiliary inputs. We introduce FlowMo, a novel
training-free guidance method that enhances motion coherence using only the
model's own predictions in each diffusion step. FlowMo first derives an
appearance-debiased temporal representation by measuring the distance between
latents corresponding to consecutive frames. This highlights the implicit
temporal structure predicted by the model. It then estimates motion coherence
by measuring the patch-wise variance across the temporal dimension and guides
the model to reduce this variance dynamically during sampling. Extensive
experiments across multiple text-to-video models demonstrate that FlowMo
significantly improves motion coherence without sacrificing visual quality or
prompt alignment, offering an effective plug-and-play solution for enhancing
the temporal fidelity of pre-trained video diffusion models.
683fca92c1e51fea3a470e93
Ariel Shaulov
claimed_verified
2025-06-05T07:28:19.299Z
65d4985d4e358ce02a949f8c
/avatars/3eda6f50d17802b1ce94349c89637e3c.svg
Ariel Shaulov
user
shaulov
683fca92c1e51fea3a470e94
Itay Hazan
extracted_confirmed
2025-06-04T06:31:03.556Z
64972b8d27e41e26a32835d4
/avatars/00c76991b5421f592d632a750ec8b998.svg
Itay Hazan
user
itayhzn
683fca92c1e51fea3a470e95
Lior Wolf
683fca92c1e51fea3a470e96
Hila Chefer
claimed_verified
2025-06-04T08:54:36.782Z
6181c72cdcc1df2c9de8a4d8
https://cdn-avatars.huggingface.co/v1/production/uploads/1655248010394-6181c72cdcc1df2c9de8a4d8.jpeg
Hila Chefer
user
Hila
FlowMo: Variance-Based Flow Guidance for Coherent Motion in Video
  Generation
Text-to-video diffusion models are notoriously limited in their ability to
model temporal aspects such as motion, physics, and dynamic interactions.
Existing approaches address this limitation by retraining the model or
introducing external conditioning signals to enforce temporal consistency. In
this work, we explore whether a meaningful temporal representation can be
extracted directly from the predictions of a pre-trained model without any
additional training or auxiliary inputs. We introduce FlowMo, a novel
training-free guidance method that enhances motion coherence using only the
model's own predictions in each diffusion step. FlowMo first derives an
appearance-debiased temporal representation by measuring the distance between
latents corresponding to consecutive frames. This highlights the implicit
temporal structure predicted by the model. It then estimates motion coherence
by measuring the patch-wise variance across the temporal dimension and guides
the model to reduce this variance dynamically during sampling. Extensive
experiments across multiple text-to-video models demonstrate that FlowMo
significantly improves motion coherence without sacrificing visual quality or
prompt alignment, offering an effective plug-and-play solution for enhancing
the temporal fidelity of pre-trained video diffusion models.
FlowMo, a training-free method, enhances motion coherence in pre-trained text-to-video diffusion models by leveraging their own predictions to reduce patch-wise temporal variance.
text-to-video diffusion models temporal aspects motion physics dynamic interactions pre-trained model FlowMo guidance method appearance-debiased temporal representation latents patch-wise variance sampling temporal fidelity