无需训练，用流模型预测直接实现高效视频动作迁移
FlowMotion: Training-Free Flow Guidance for Video Motion Transfer
- 直接利用流模型的潜在预测生成运动引导信号
- 相比现有方法提速3倍以上，资源消耗更低
- 适合追求快速部署与高灵活性的动作迁移场景
视频动作迁移旨在生成继承源视频运动模式但呈现新场景的目标视频。现有无训练方法依赖预训练文本到视频（T2V）模型的中间输出构建运动引导，导致计算开销大、灵活性差。本文提出FlowMotion，一种全新的无训练框架，通过直接利用基于流的T2V模型的预测输出实现高效灵活的动作迁移。核心洞察是早期潜在预测天然包含丰富的时序信息。我们提出流动引导机制，基于潜在预测提取运动表征，对齐源视频与生成视频间的运动模式。进一步引入速度正则化策略以稳定优化过程，确保运动演化平滑。整个流程仅作用于模型预测结果，显著提升时间与资源效率，性能优于当前最佳方法。
原文摘要 · Abstract (English)
Video motion transfer aims to generate a target video that inherits motion patterns from a source video while rendering new scenes. Existing training-free approaches focus on constructing motion guidance based on the intermediate outputs of pre-trained T2V models, which results in heavy computational overhead and limited flexibility. In this paper, we present FlowMotion, a novel training-free framework that enables efficient and flexible motion transfer by directly leveraging the predicted outputs of flow-based T2V models. Our key insight is that early latent predictions inherently encode rich temporal information. Motivated by this, we propose flow guidance, which extracts motion representations based on latent predictions to align motion patterns between source and generated videos. We further introduce a velocity regularization strategy to stabilize optimization and ensure smooth motion evolution. By operating purely on model predictions, FlowMotion achieves superior time and resource efficiency as well as competitive performance compared with state-of-the-art methods.
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