Koala-36M: A Large-scale Video Dataset Improving Consistency between Fine-grained Conditions and Video Content

TL;DR

Koala-36M is a large-scale, high-quality video dataset designed to improve the alignment between detailed conditions and video content, enhancing the performance of video generation models.

Contribution

The paper introduces Koala-36M, a novel dataset with accurate temporal splitting, detailed captions, and quality filtering, addressing limitations of existing datasets.

Findings

Enhanced temporal consistency through a linear classifier for transition detection.

Structured captions averaging 200 words improve text-video alignment.

Filtering high-quality videos with VTSS boosts dataset reliability.

Abstract

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 at https://koala36m.github.io/.