VidProM: A Million-scale Real Prompt-Gallery Dataset for Text-to-Video Diffusion Models
<|reference_start|>VidProM: A Million-scale Real Prompt-Gallery Dataset for Text-to-Video Diffusion Models: The arrival of Sora marks a new era for text-to-video diffusion models, bringing significant advancements in video generation and potential applications. However, Sora, along with other text-to-video diffusion models, is highly reliant on prompts, and there is no publicly available dataset that features a study of text-to-video prompts. In this paper, we introduce VidProM, the first large-scale dataset comprising 1.67 Million unique text-to-Video Prompts from real users. Additionally, this dataset includes 6.69 million videos generated by four state-of-the-art diffusion models, alongside some related data. We initially discuss the curation of this large-scale dataset, a process that is both time-consuming and costly. Subsequently, we underscore the need for a new prompt dataset specifically designed for text-to-video generation by illustrating how VidProM differs from DiffusionDB, a large-scale prompt-gallery dataset for image generation. Our extensive and diverse dataset also opens up many exciting new research areas. For instance, we suggest exploring text-to-video prompt engineering, efficient video generation, and video copy detection for diffusion models to develop better, more efficient, and safer models. The project (including the collected dataset VidProM and related code) is publicly available at https://vidprom.github.io under the CC-BY-NC 4.0 License.<|reference_end|>
@article{wang2024vidprom:,
    title={VidProM: A Million-scale Real Prompt-Gallery Dataset for Text-to-Video
  Diffusion Models},
    author={Wenhao Wang, Yi Yang},
    journal={arXiv preprint arXiv:2403.06098},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.06098},
    primaryClass={cs.CV cs.CL}
}