Tiger200K: Manually Curated High Visual Quality Video Dataset from UGC Platform

• URL: http://arxiv.org/abs/2504.15182v1
• Date: Mon, 21 Apr 2025 15:44:06 GMT
• Title: Tiger200K: Manually Curated High Visual Quality Video Dataset from UGC Platform
• Authors: Xianpan Zhou,
• Abstract summary: Tiger200K is a manually curated high visual quality video dataset sourced from User-Generated Content (UGC) platforms.<n>By prioritizing visual fidelity and aesthetic quality, Tiger200K underscores the critical role of human expertise in data curation.<n>The dataset will undergo ongoing expansion and be released as an open-source initiative to advance research and applications in video generative models.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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/

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