videogen
Datasets
All datasets matching “videogen”Tom-and-Jerry-VideoGeneration-Dataset中文阅读
Information
The dataset contains about 6000 scenes sample,
lr: 1E-3 betas: [ 0.8, 0.95 ] eps: 1e-8 weight_decay: 1e-4
After 4000 iterations, all generated content will tend to the target sample
The length of each video is 6 seconds.
The frame rate of the videos is 14 frames per second.
The video resolution is w=540 , h=360.
Dataset Format
.
├── README.md
├── captions.txt
├── videos
└── videos.txt
Used
import os
from datasets import Dataset… See the full description on the dataset page: https://huggingface.co/datasets/Wild-Heart/Tom-and-Jerry-VideoGeneration-Dataset.Disney-VideoGeneration-Dataset
Steamboat Willie - Video Generation Dataset
中文阅读
This dataset contains 69 videos clipped from Disney's Steamboat Willie.
The length of each video is 6 seconds.
The frame rate of the videos is 30 frames per second.
The video resolution is 640 x 380.
All videos are black and white, not in color.
Dataset Format
.
├── README.md
├── metadata.csv
├── prompt.txt
├── videos
└── videos.txt
The prompt.txt file contains descriptions for each video, each description containing… See the full description on the dataset page: https://huggingface.co/datasets/Wild-Heart/Disney-VideoGeneration-Dataset.video-gen-physics-gallery
Paper material (assets/drive)
Two generated galleries plus a figure asset pack. All reproducible from the repo —
don't hand-edit them, re-run the generator.
gallery
what it shows
generator
real_videos/
the input side of the benchmark: real GT episodes, 5 diverse tasks per (embodiment x view-track x markovian/non-markovian) cell
scripts/make_real_video_gallery.py
method_comparison/
the output side: ONE episode rendered by every acceleration method, so a single dir is… See the full description on the dataset page: https://huggingface.co/datasets/doanh25032004/video-gen-physics-gallery.videogenSpotlight-VideoGen-Errors
Spotlight Dataset
Spotlight: Identifying and Localizing Video Generation Errors Using VLMs
Aditya Chinchure, Sahithya Ravi, Pushkar Shukla, Vered Shwartz, Leonid Sigal
🎉 Accepted to ECCV 2026
🌐 Project Page
Summary
Spotlight is a benchmark for evaluating whether Vision Language Models (VLMs) can precisely
localize and explain errors in AI-generated videos. It contains 600 videos generated by
three state-of-the-art Text-to-Video (T2V) models —… See the full description on the dataset page: https://huggingface.co/datasets/UBC-ViL/Spotlight-VideoGen-Errors.video_gen_physics