Beyond the Golden Data: Resolving the Motion-Vision Quality Dilemma via Timestep Selective Training

ArXi:2603.25527v1 Announce Type: new Recent advances in video generation models have achieved impressive results. However, these models heavily rely on the use of high-quality data that combines both high visual quality and high motion quality. In this paper, we identify a key challenge in video data curation: the Motion-Vision Quality Dilemma. We discovered that visual quality and motion intensity inherently exhibit a negative correlation, making it hard to obtain golden data that excels in both aspects.