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

翻译：尽管近期的基础视频生成器能够产生视觉丰富的输出，但它们仍然难以克服外观漂移问题，即物体在帧间逐渐退化或发生不一致变化，从而破坏了视觉连贯性。我们假设这是因为在特征层面缺乏明确的空间追踪监督。我们提出了Track4Gen，一种具有空间感知能力的视频生成器，它将视频扩散损失与跨帧点追踪相结合，为扩散特征提供了增强的空间监督。Track4Gen通过对现有视频生成架构进行最小改动，将视频生成和点追踪任务融合到一个单一网络中。以Stable Video Diffusion为骨干网络，Track4Gen证明了将通常作为独立任务处理的视频生成与点追踪统一起来是可行的。我们广泛的评估表明，Track4Gen能有效减少外观漂移，从而生成时间稳定且视觉连贯的视频。项目页面：hyeonho99.github.io/track4gen