VidGen-1M: A Large-Scale Dataset for Text-to-video Generation
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.
66b19948dc60939477270769
Zhiyu Tan
66b19948dc6093947727076a
Xiaomeng Yang
66b19948dc6093947727076b
Luozheng Qin
admin_assigned
2024-08-06T12:05:59.032Z
66a9b3533d417b0baa9220a6
/avatars/adc372bd24df1d3bf43258833411e8af.svg
Luozheng Qin
user
Fr0zencr4nE
66b19948dc6093947727076c
Hao Li
VidGen-1M: A Large-Scale Dataset for Text-to-video Generation
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.
A new, high-quality dataset called VidGen-1M improves training for text-to-video models by ensuring better video quality, detailed captions, and temporal consistency.
video-text pairs text-to-video models temporal consistency video quality caption quality data imbalance image models manual rule-based curation coarse-to-fine curation strategy video generation model