title: VidGen-1M: A Large-Scale Dataset for Text-to-video Generation
summary: The quality of video-text pairs fundamentally determines the upper bound of
  text-to-video models. Currently, the datasets used for training these models
  suffer from significant shortcomings, including low temporal consistency,
  poor-quality captions, substandard video quality, and imbalanced data
  distribution. The prevailing video curation process, which depends on image
  models for tagging and manual rule-based curation, leads to a high
  computational load and leaves behind unclean data. As a result, there is a lack
  of appropriate training datasets for text-to-video models. To address this
  problem, we present VidGen-1M, a superior training dataset for text-to-video
  models. Produced through a coarse-to-fine curation strategy, this dataset
  guarantees high-quality videos and detailed captions with excellent temporal
  consistency. When used to train the video generation model, this dataset has
  led to experimental results that surpass those obtained with other models.
paper_authors._id: 66b19948dc60939477270769
paper_authors.name: Zhiyu Tan
paper_authors._id: 66b19948dc6093947727076a
paper_authors.name: Xiaomeng Yang
paper_authors._id: 66b19948dc6093947727076b
paper_authors.name: Luozheng Qin
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-08-06T12:05:59.032Z
paper_authors._id: 66a9b3533d417b0baa9220a6
paper_authors.avatarUrl: /avatars/adc372bd24df1d3bf43258833411e8af.svg
paper_authors.fullname: Luozheng Qin
paper_authors.type: user
paper_authors.user: Fr0zencr4nE
paper_authors._id: 66b19948dc6093947727076c
paper_authors.name: Hao Li
paper_title: VidGen-1M: A Large-Scale Dataset for Text-to-video Generation
paper_summary: The quality of video-text pairs fundamentally determines the upper bound of
  text-to-video models. Currently, the datasets used for training these models
  suffer from significant shortcomings, including low temporal consistency,
  poor-quality captions, substandard video quality, and imbalanced data
  distribution. The prevailing video curation process, which depends on image
  models for tagging and manual rule-based curation, leads to a high
  computational load and leaves behind unclean data. As a result, there is a lack
  of appropriate training datasets for text-to-video models. To address this
  problem, we present VidGen-1M, a superior training dataset for text-to-video
  models. Produced through a coarse-to-fine curation strategy, this dataset
  guarantees high-quality videos and detailed captions with excellent temporal
  consistency. When used to train the video generation model, this dataset has
  led to experimental results that surpass those obtained with other models.