FlowMo: Variance-Based Flow Guidance for Coherent Motion in Video Generation (2506.01144v2)

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.

An Expert Overview of "FlowMo: Variance-Based Flow Guidance for Coherent Motion in Video Generation"

The paper "FlowMo: Variance-Based Flow Guidance for Coherent Motion in Video Generation" introduces a novel approach for enhancing temporal coherence in text-to-video diffusion models. The authors address a pivotal challenge inherent in these models: their significant limitations in capturing dynamic temporal elements accurately. FlowMo presents a training-free method that leverages the model's internal representations to achieve improved motion coherence without the need for retraining or external conditioning signals.

Key Contributions

1. Training-Free Motion Guidance: FlowMo distinguishes itself by offering a plug-and-play inference-time guidance method. Traditional approaches in video generation require retraining with temporal consistency objectives or involve complex architectures and external inputs like optical flow signals. FlowMo simplifies this process by deriving guidance signals directly from the model’s latent predictions in each diffusion step. This approach substantially reduces computational overhead and allows for straightforward integration into existing pipelines.
2. Variance-Based Coherence Metric: The method utilizes patch-wise variance across consecutive frames as a surrogate for motion coherence. This metric is derived from measuring the distance between latent predictions. High variance is indicative of incoherent motion, characterized by abrupt changes or visual artifacts, whereas low variance corresponds to smooth, coherent transitions. FlowMo thus guides the model to minimize these variances dynamically.
3. Empirical Validation: Through extensive experiments, the authors demonstrate FlowMo's ability to improve motion coherence significantly across several models, including Wan2.1 and CogVideoX. The results highlight improvements in motion quality and overall video fidelity without compromising visual aesthetics or text alignment. Importantly, FlowMo provides a substantial performance boost in human preference studies and automated VBench evaluations.

Implications and Future Directions

FlowMo’s approach opens several avenues for advancing video generation technologies. It highlights the potential of using model-internal dynamics—such as temporal latent representations—over external signals, thus promising more efficient and adaptable solutions. Practically, FlowMo can be integrated into current-generation systems where retraining might be infeasible due to resource constraints.

Theoretically, the paper paves the way for further exploration into leveraging latent spaces in generative models. Future work could delve into enhancing temporal signal extraction and refining these guidance methods for more specific application domains, such as real-time video synthesis and improved video editing tools.

Moreover, as video diffusion models evolve, incorporating FlowMo into the training phase—potentially as an embedded temporal loss—could yield even richer temporal representations. Addressing the limitations identified in the paper, such as inference-time computational costs and extending the method to capture novel motion types, could enhance the scope and applicability of FlowMo.

Conclusion

FlowMo represents a notable advance in mitigating temporal artifacts in text-to-video generation. By focusing on training-free methodologies and maximizing the utility of latent spaces, the authors offer a versatile and impactful solution for enhancing motion coherence. This research not only addresses a core challenge in video synthesis but also sets the stage for more adaptive generative frameworks in AI, emphasizing the importance of internal model dynamics in achieving desirable output characteristics.