datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
veo3-video-prompts
Veo 3 Video Generation Dataset
English | Português do Brasil
English
Summary
A collection of AI-generated videos created with Google's Veo 3 family of models. Each record contains the original text prompt, the model variant used, the generated video, and (when applicable) the input reference image. Videos are organized into one configuration per model variant.
Videos: 5,811
Input images: 1,354
Configurations: 6
Language of prompts: multilingual… See the full description on the dataset page: https://huggingface.co/datasets/artificialguybr/veo3-video-prompts.text-2-video-human-preferences-veo3
Rapidata Video Generation Veo 3 Human Preference
In this dataset, ~46k human responses from ~20k human annotators were collected to evaluate Veo3 video generation model on our benchmark. This dataset was collected in roughly 35 minutes using the Rapidata Python API, accessible to anyone and ideal for large scale data annotation.
Explore our latest model rankings on our website.
If you get value from this dataset and would like to see more in the future, please consider liking… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/text-2-video-human-preferences-veo3.vla-data-aug-veo31-test
VLA Data Augmentation (Veo 3.1)
Synthetic video samples for vision-language-action (VLA) data augmentation: 8s clips generated with Veo 3.1 on Vertex AI, with per-clip frames and prompt metadata.
Dataset summary
Model: veo-3.1-generate-001
Duration: 8 seconds per clip, 16:9, no audio
Content: Multi-scene prompts (meetings, care, logistics, office, etc.)
First batch (runs/): 11 prompts × 6 clips each (English prompts); see RUN_PLAN_5D_11PROMPTS_X6.md. 補齊至每題 18… See the full description on the dataset page: https://huggingface.co/datasets/sou35/vla-data-aug-veo31-test.text-2-video-human-preferences-veo3.1
Rapidata Video Generation Veo 3.1 Human Preference
In this dataset, ~74k human responses from ~23k human annotators were collected to evaluate the Veo 3.1 video generation model on our benchmark. This dataset was collected using the Rapidata Python API, accessible to anyone and ideal for large scale data annotation.
Explore our latest model rankings on our website.
If you get value from this dataset and would like to see more in the future, please consider liking it ❤️… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/text-2-video-human-preferences-veo3.1.