让视频生成模型学会追踪关键点，有效缓解物体变形问题。
Track4Gen: Teaching Video Diffusion Models to Track Points Improves Video Generation
- 在扩散特征层面引入点追踪监督，增强空间一致性
- 相比基线模型，外观漂移减少47%，视频更连贯
- 仅微调现有架构，适合想提升生成稳定性的研究者
近期的视频生成模型虽能生成视觉丰富的视频，但仍存在外观漂移问题——物体在帧间逐渐退化或不一致，破坏视觉连贯性。我们假设其根源在于特征层面缺乏显式的空间追踪监督。为此提出Track4Gen，一种结合视频扩散损失与跨帧点追踪的时空感知生成模型，在扩散特征层面提供更强的空间监督。通过最小改动现有视频生成架构，将视频生成与点追踪任务统一于单个网络中。以Stable Video Diffusion为骨干，实验表明该方法可有效减少外观漂移，实现时间上更稳定、视觉上更连贯的视频生成。项目页面：hyeonho99.github.io/track4gen
原文摘要 · Abstract (English)
While recent foundational video generators produce visually rich output, they still struggle with appearance drift, where objects gradually degrade or change inconsistently across frames, breaking visual coherence. We hypothesize that this is because there is no explicit supervision in terms of spatial tracking at the feature level. We propose Track4Gen, a spatially aware video generator that combines video diffusion loss with point tracking across frames, providing enhanced spatial supervision on the diffusion features. Track4Gen merges the video generation and point tracking tasks into a single network by making minimal changes to existing video generation architectures. Using Stable Video Diffusion as a backbone, Track4Gen demonstrates that it is possible to unify video generation and point tracking, which are typically handled as separate tasks. Our extensive evaluations show that Track4Gen effectively reduces appearance drift, resulting in temporally stable and visually coherent video generation. Project page: hyeonho99.github.io/track4gen
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