针对视频生成数据集优化，提出高效清洗与增强方法。
Technical Report: Competition Solution For Modelscope-Sora
- 通过生成描述、过滤冗余内容提升数据质量
- 在有限算力下实现高质量视频训练数据构建
- 适合关注文本到视频生成的数据工程人员
本报告介绍了Modelscope-Sora挑战中采用的方法，聚焦于视频生成模型的微调数据优化。挑战评估参赛者在特定计算约束下，对文本到视频任务的数据进行分析、清洗与生成的能力。方法包括视频描述生成、数据过滤及加速处理等数据处理技术。报告详细说明了用于提升训练数据质量的流程与工具，确保文本到视频生成模型性能的提升。
原文摘要 · Abstract (English)
This report presents the approach adopted in the Modelscope-Sora challenge, which focuses on fine-tuning data for video generation models. The challenge evaluates participants' ability to analyze, clean, and generate high-quality datasets for video-based text-to-video tasks under specific computational constraints. The provided methodology involves data processing techniques such as video description generation, filtering, and acceleration. This report outlines the procedures and tools utilized to enhance the quality of training data, ensuring improved performance in text-to-video generation models.
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