With the continuous progress of visual generation technologies, the scale of video datasets has grown exponentially. The quality of these datasets plays a pivotal role in the performance of video generation models. We assert that temporal splitting, detailed captions, and video quality filtering are three crucial determinants of dataset quality. However, existing datasets exhibit various limitations in these areas. To address these challenges, we introduce Koala-36M, a large-scale, high-quality video dataset featuring accurate temporal splitting, detailed captions, and superior video quality. The essence of our approach lies in improving the consistency between fine-grained conditions and video content. Specifically, we employ a linear classifier on probability distributions to enhance the accuracy of transition detection, ensuring better temporal consistency. We then provide structured captions for the splitted videos, with an average length of 200 words, to improve text-video alignment. Additionally, we develop a Video Training Suitability Score (VTSS) that integrates multiple sub-metrics, allowing us to filter high-quality videos from the original corpus. Finally, we incorporate several metrics into the training process of the generation model, further refining the fine-grained conditions. Our experiments demonstrate the effectiveness of our data processing pipeline and the quality of the proposed Koala-36M dataset. Our dataset and code have been released atthis https URL.
View on arXiv@article{wang2025_2410.08260,
title={ Koala-36M: A Large-scale Video Dataset Improving Consistency between Fine-grained Conditions and Video Content },
author={ Qiuheng Wang and Yukai Shi and Jiarong Ou and Rui Chen and Ke Lin and Jiahao Wang and Boyuan Jiang and Haotian Yang and Mingwu Zheng and Xin Tao and Fei Yang and Pengfei Wan and Di Zhang },
journal={arXiv preprint arXiv:2410.08260},
year={ 2025 }
}