Beyond the Golden Data: Resolving the Motion-Vision Quality Dilemma via Timestep Selective Training
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. To address this challenge, we first examine the hierarchical learning dynamics of video diffusion models and conduct gradient-based analysis on quality-degraded samples. We discover that quality-imbalanced data can produce ...
| Authors | Xiangyang Luo, Qingyu Li, Yuming Li et al. |
|---|---|
| Primary category | cs.CV |
| Published | 26 March 2026 |
| Last updated at source | 1 April 2026 |
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