Tiger200K: Manually Curated High Visual Quality Video Dataset from UGC Platform
This addresses the problem of limited open training data for video generation researchers, though it is incremental as it builds on existing curation methods.
The authors tackled the lack of high-quality open datasets for text-to-video generation by creating Tiger200K, a manually curated video dataset from UGC platforms, resulting in a dataset of 200,000 videos with improved visual fidelity and temporal consistency for fine-tuning models.
The recent surge in open-source text-to-video generation models has significantly energized the research community, yet their dependence on proprietary training datasets remains a key constraint. While existing open datasets like Koala-36M employ algorithmic filtering of web-scraped videos from early platforms, they still lack the quality required for fine-tuning advanced video generation models. We present Tiger200K, a manually curated high visual quality video dataset sourced from User-Generated Content (UGC) platforms. By prioritizing visual fidelity and aesthetic quality, Tiger200K underscores the critical role of human expertise in data curation, and providing high-quality, temporally consistent video-text pairs for fine-tuning and optimizing video generation architectures through a simple but effective pipeline including shot boundary detection, OCR, border detecting, motion filter and fine bilingual caption. The dataset will undergo ongoing expansion and be released as an open-source initiative to advance research and applications in video generative models. Project page: https://tinytigerpan.github.io/tiger200k/