title: FlowMo: Variance-Based Flow Guidance for Coherent Motion in Video
    Generation
summary: 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.
paper_authors._id: 683fca92c1e51fea3a470e93
paper_authors.name: Ariel Shaulov
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-05T07:28:19.299Z
paper_authors._id: 65d4985d4e358ce02a949f8c
paper_authors.avatarUrl: /avatars/3eda6f50d17802b1ce94349c89637e3c.svg
paper_authors.fullname: Ariel Shaulov
paper_authors.type: user
paper_authors.user: shaulov
paper_authors._id: 683fca92c1e51fea3a470e94
paper_authors.name: Itay Hazan
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-06-04T06:31:03.556Z
paper_authors._id: 64972b8d27e41e26a32835d4
paper_authors.avatarUrl: /avatars/00c76991b5421f592d632a750ec8b998.svg
paper_authors.fullname: Itay Hazan
paper_authors.type: user
paper_authors.user: itayhzn
paper_authors._id: 683fca92c1e51fea3a470e95
paper_authors.name: Lior Wolf
paper_authors._id: 683fca92c1e51fea3a470e96
paper_authors.name: Hila Chefer
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-04T08:54:36.782Z
paper_authors._id: 6181c72cdcc1df2c9de8a4d8
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1655248010394-6181c72cdcc1df2c9de8a4d8.jpeg
paper_authors.fullname: Hila Chefer
paper_authors.type: user
paper_authors.user: Hila
paper_title: FlowMo: Variance-Based Flow Guidance for Coherent Motion in Video
    Generation
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: 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