Title: FlowMo: Variance-Based Flow Guidance for Coherent Motion in Video Generation
Venue: NeurIPS 2025
Accepted: yes
Keywords: video generation

diffusion models

generative models
Abstract: 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.


Peer reviews:
comments: SUMMARY:
This paper presents a training free method for producing coherent videos from a pretrained video generation flow matching model. The idea is to introduce a refinement optimization when sampling the denoising steps that encourages more coherent frame to frame motions.

STRENGTHS AND WEAKNESSES:
Strengths: 

* Training-free
* The paper does a nice job of showing why a patch-based variance reduction approach can be an effective refinement strategy. I appeciate the experiment described in Section 3.2.

Weaknesses:

* The refinement optimization introduces a 2.39x increase in runtime to perform the gradient descent step. This is very briefly mentioned in the limitations section of the paper. I think this needs to be brought up as part of the results and evaluation. The authors mention that this optimization could also be made more efficient by incorporating it into training. I think the work would be stronger if it showed how adding it to training improves results.

QUESTIONS:
The overall idea of the work is simple and nice. But the core idea of adding a refinement optimization based on frame to frame variance in patches seems like it should be tested not just at inference time, but also in the context of training. I would enourage the authors to include in the rebuttal some results for incorporating it into training. How good are the resulting videos in terms of coherence? What is the inference runtime in this setting?

LIMITATIONS:
Yes
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-06-24

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper focuses on enhancing the temporal coherence of text-to-video diffusion models. The authors introduce FlowMo, a novel training-free guidance method that aims to improve motion coherence without additional training or external inputs. The main contributions include the Appearance-Debiased Temporal Representation to measure the distances between consecutive frame latents, and Motion Coherence Estimation to guide the model towards reducing temporal inconsistencies during sampling. Experiments conducted on VBench and VideoJAM-bench show reasonable improvements over the baselines without FlowMo.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n+ Tested across various models like Wan2.1-1.3B and CogVideoX-5B to showcase the method's generalizability.\n+ The method does not require retraining or external conditioning signals, making it a versatile solution for improving existing models.\n+ The paper is mostly clear and easy to follow.\n\nWeaknesses:\n- The technical contribution is limited. As a training-free method, the main idea of FlowMo is to improve the temporal consistency by explicitly reducing the difference between consecutive frames. In this way, this method is more like a trade-off between temporal consistency and dynamic degree. It is evidenced by the degraded Dynamic Degree metric of FlowMo on VBench. Therefore, the technical contribution of this paper is limited.\n- The ablation study is needed. The proposed FlowMo is a manually designed rule to refine diffusion model prediction. There are many manually-determined designs in FlowMo including the refinement steps, the calculation of patch-wise variance  and FlowMo loss. The ablation study of how these designs influence the performances should be included.\n- The officially reported total score of Wan2.1-1.3B on VBench is 84.26%. However, in this paper, the performance of Wan2.1-1.3B is improved from 75.14% (without FlowMo) to 81.34% (with FlowMo). I speculate that this large gap may be caused by different inference settings (e.g., output resolution, prompt refinement). Since the performance of Wan2.1-1.3B baseline is much lower the official version, the conclusion in this paper is not convincing enough.\n- The results might be influenced by external factors such as the complexity of the input text prompts or the diversity of the generated content. For the prompt that requires large motion or complex transition, the continuous constrain may affect the results.\n- The longer inference time than baseline may limit the application of FlowMo on larger model.\n\nQUESTIONS:\n1. The questions in weaknesses part.\n2. How well does FlowMo perform on models with different architectures or on tasks requiring more nuanced temporal reasoning?\n3. Could external factors, such as the quality or specificity of the input text, affect the performance of FlowMo in generating temporally coherent videos?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 1, "ORIGINALITY": 2, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper aims to enhance motion coherence of text-to-video diffusion models in a training-free way. Text-to-video diffusion models often struggle to accurately capture temporal dynamics like motion and interactions. Existing solutions require retraining or external signals to improve temporal consistency, which can be resource-intensive and complex. FlowMo introduces a training-free guidance approach that leverages the model’s own predictions at each diffusion step to extract appearance-debiased temporal representations and dynamically guide the model to enhance motion coherence by reducing temporal variance. Comprehensive experiments on various text-to-video models show that FlowMo effectively boosts motion coherence without compromising visual quality or prompt alignment, providing an easy-to-integrate improvement for existing models.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. The quantitative and qualitative motivation analyses are interesting and convincing. The demos in webpage make it clear for readers to understand how the proposed method works.\n2. The proposed method is simple and easy to be implemented. The training-free design allows readers to validate the effectiveness of this method easily and inexpensively on their own tasks.\n3. The writing is easy to understand.\n\nWeaknesses\n1. Computation time is increased by x2.39 when using FlowMo, which will undermine the cost-effectiveness of the method in practical applications.\n2. The quantitative and qualitative comparisons with FreeInit should be conduct and displayed in the main text, since it is similar and related to this paper. Instead, this paper majorly compares FreeInit in appendix, which is not very clear for readers.\n3. The ablation study chapter is not very clear and precise, since there are no quantitative ablation results.\n4. There are some confusing statements. For instance, in terms of the line.3 of Algorithm.1, the first term of the right equation is written as conditioned on P, which is inconsistent with the definition of the CFG formula.\n\nQUESTIONS:\nSuggestions:\n1. Discuss the cost-effectiveness of FlowMo and its proper applications.\n2. Compare FreeInit quantitatively and qualitatively in the main text. And it would be better if more training-free works could be taken into comparison.\n3. Add quantitative ablation results to make the paper claims solid.\n4. Conduct a careful writing check.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces a training-free inference-time guidance method to improve temporal coherence in text-to-video diffusion models. The key insight is that temporally coherent motion should exhibit low patch-wise variance across frames in the model's latent space.   \nThe paper computes appearance-debiased temporal signals by measuring L1 distances between consecutive latent frames. Then the paper measures the motion coherence by calculating patch-wise variance across the temporal dimension as a proxy. By gradient-based optimization of the input latent during specific denoising timesteps, the maximal patchwise variance is minimized and thus the temporal coherence is improved.   \nThe method is evaluated on Wan2.1-1.3B and CogVideoX-5B models, showing consistent improvements in motion coherence while maintaining visual quality and text alignment.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n\n1. The insight of the paper is valuable. The core insight that temporal coherence correlates with low patch-wise variance in latent space is intuitive and well-supported by both qualitative and quantitative analysis. This finding is well-supported by experiments in the Section3.2. \n2. The proposed method is training-free, making it a practical plug-and-play solution for existing models.\n3. The evaluation is comprehensive. The paper includes thorough experiments with both automatic metrics (VBench) and human evaluations, showing consistent improvements across multiple criteria.\n\n\nWeaknesses\n1. The method would cause a significant 2.39× increase in inference time due to gradient computation and optimization steps. \n2. The choice of maximizing over patch-wise variances (Equation 8) appears somewhat ad-hoc. While ablations show it works better than mean aggregation, the theoretical justification could make the paper stronger.\n3. The selection of timesteps 1-12 for optimization is based on empirical observation rather than principled analysis. Different models or architectures might require different timestep ranges.\n\nQUESTIONS:\n1. Timestep Generalization: How sensitive is the method to the choice of optimization timesteps? Have you explored adaptive timestep selection based on the model's temporal dynamics?\n2. Computational Efficiency: Have you investigated more efficient optimization strategies (e.g., fewer gradient steps, approximate gradients) that could reduce the computational overhead while maintaining effectiveness?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}