FlowMo: Variance-Based Flow Guidance for Coherent Motion in Video
  Generation
Authors: name: Ariel Shaulov, name: Itay Hazan, name: Lior Wolf, name: Hila Chefer

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.