不修改模型就能让视频生成更连贯，靠的是修复注意力图的时序结构。
TeDiO: Temporal Diagonal Optimization for Training-Free Coherent Video Diffusion
- 通过分析注意力图中的对角线模式，识别运动不连贯区域。
- 在推理阶段轻量更新隐空间，提升帧间动态一致性。
- 无需训练或外部监督，可直接接入现有视频扩散模型。
近期的文本到视频扩散变换器虽能生成视觉逼真的帧，但在时序连贯性上仍存在问题，常出现闪烁、漂移或运动不稳定现象。我们发现这些失败会在模型内部留下明显痕迹：不连贯的视频在中间自注意力图中表现出不规则、碎片化的时序对角线，而稳定运动则对应平滑的带状对角线模式。基于此观察，我们提出TeDiO，一种无需训练、仅在推理阶段运行的方法，通过正则化内部注意力模式来增强时序一致性。TeDiO估计对角线平滑度，识别不稳定的区域，并执行轻量级隐空间更新，促进连贯的帧间动态，无需修改模型权重或使用外部运动监督。在多个视频扩散模型（如Wan2.1、CogVideoX）上，TeDiO显著提升了运动平滑性，同时保持单帧视觉质量，为现代视频生成系统提供高效即插即用的动态真实感优化方案。
原文摘要 · Abstract (English)
Recent text-to-video diffusion transformers generate visually compelling frames, yet still struggle with temporal coherence, often producing flickering, drifting, or unstable motion. We show that these failures leave a clear imprint inside the model: incoherent videos consistently exhibit irregular, fragmented temporal diagonals in their intermediate self-attention maps, whereas stable motion corresponds to smooth, band-diagonal patterns. Building on this observation, we introduce TeDiO, a training-free, inference-time method that reinforces temporal consistency by regularizing these internal attention patterns. TeDiO estimates diagonal smoothness, identifies unstable regions, and performs lightweight latent updates that promote coherent frame-to-frame dynamics, without modifying model weights or using external motion supervision. Across multiple video diffusion models (e.g., Wan2.1, CogVideoX), TeDiO delivers markedly smoother motion while preserving per-frame visual quality, offering an efficient plug-and-play approach to improving dynamic realism in modern video generation systems.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。