Controlled study finds training data quality is decisive for text-to-video models
Amber Yijia Zheng · hf · 2026-07-23
Moving Alphabet studies how training data affects text-to-video generation, using a controlled procedural testbed.
The paper renders letters with controlled variation in font, color, size, position, direction, and speed, then corrupts metadata to isolate the impact of data distribution and caption quality. The main findings are:
- Balanced, diverse video content and duration distribution are critical for generalization.
- Caption quality strongly affects both performance and training efficiency, suggesting text-to-video models are limited by video understanding.
- Classifier-free guidance and fine-tuning on cleaner data help recover some performance, but cannot fully fix poor pre-training data.
The authors argue that pre-training data deserves more scientific attention in large-scale video generation.
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