datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Motion-o-MCoT-PLM-motion-keyframes
Motion-o-MCoT (PLM + motion keyframes)
Subset of STGR: STR_plm_rdcap rows with <motion in reasoning_process, plus sharded keyframes under videos/stgr/plm/kfs/.
Train split: 3,168 examples (see export_manifest.json in the repo for exact export stats).
Keyframes: JPEGs are stored under shard subfolders (e.g. videos/stgr/plm/kfs/plm_0150/…) so each directory stays under Hugging Face file-count limits. Each key_frames[].path in the JSON is relative to videos/stgr/plm/kfs/ (e.g.… See the full description on the dataset page: https://huggingface.co/datasets/bishoygaloaa/Motion-o-MCoT-PLM-motion-keyframes.human-motion-tracking-deeplabcutThis dataset is used to adapt DeepLabCut for Human motion tracking.
Structure of the dataset
videos contains 100+ videos of 4 candidates recorded during a game of darts.
labeled-data contains labels on the corresponding frames of the videos. These labels are used to adapt DeepLabCut for human motion tracking. Under labeled-data there are 2 folders for every video.
video_name has all the relevant frames extracted from the video, xy coordinates of the labels in the csv file and the… See the full description on the dataset page: https://huggingface.co/datasets/GT-Neuronext/human-motion-tracking-deeplabcut.human-motion-tracking-deeplabcutThis dataset is used to adapt DeepLabCut for Human motion tracking.
Structure of the dataset
videos contains 100+ videos of 4 candidates recorded during a game of darts.
labeled-data contains labels on the corresponding frames of the videos. These labels are used to adapt DeepLabCut for human motion tracking. Under labeled-data there are 2 folders for every video.
video_name has all the relevant frames extracted from the video, xy coordinates of the labels in the csv file and the… See the full description on the dataset page: https://huggingface.co/datasets/pratikshapai/human-motion-tracking-deeplabcut.QtMeshEditor-motion-corpus
QtMeshEditor Motion Corpus
A permissively-licensed animated-humanoid corpus: rigged 3D characters
with skeletal animation clips, harvested for
QtMeshEditor's text-to-motion
v2 work (epic #837)
— the template clip library and the training set for a from-scratch
flow-matching motion model.
Every asset is CC0 or CC-BY — nothing here derives from Mixamo, LAFAN1,
Bandai-Namco, AMASS/HumanML3D, or game rips (all license-poisoned for
commercial redistribution). That makes this corpus —… See the full description on the dataset page: https://huggingface.co/datasets/fernandotonon/QtMeshEditor-motion-corpus.ImageNet-C-motion_blur-severity_5MotionEdit-TrainMotionMillion
🔑 Key Features
Over 2000 hours of high-quality human motion captured from web-scale human video data, covering:
Martial Arts (23.7%)
Fitness (26.4%)
Performance (17.5%)
Dance (14.9%)
Non-Human (2.9%)
Sports (2.4%)
Over 20 detailed annotations per motion, including:
Age
Body Characteristics
Movement Styles
Emotions
Environments
👨🏫 Get Started
Download the Dataset
To download the full dataset, use the following code. If you encounter any… See the full description on the dataset page: https://huggingface.co/datasets/InternRobotics/MotionMillion.LIBERO-motionshape-of-motion
shape-of-motion
Complete: all source file paths and sizes verified.
Copy of the supplied local shape-of-motion directory, preserving all relative file paths.
Source inventory: 8,732 files, 1,258,797,388 bytes.
The output/ directory contains nine run-output directories, including their checkpoints and other saved outputs. No conversion, resampling, or file deletion was performed.
Motion324clr_motion_planning_hw_7champ_motions_example
Example data for Champ inference
Links
github: https://github.com/fudan-generative-vision/champ
models: https://huggingface.co/fudan-generative-ai/champ
vit-motion-datasetmotiontrans
MotionTrans Dataset
[MotionTrans Main Repository]
Download
Please download this repository and unzip motiontrans_dataset.zip.
Introduction
MotionTrans is the first framework to achieve explicit end-to-end human-to-robot motion transfer, enabling motion-level policy learning directly from human data. This repository contains the MotionTrans Dataset used in our paper, including zero-shot human-robot cotraining data and finetuning data. The dataset provides 15… See the full description on the dataset page: https://huggingface.co/datasets/michaelyuanqwq/motiontrans.MotionEdit-BenchMotionEdit-Bench is a benchmark dataset for evaluating image editing models on the task of Motion Image Editing, a novel text-based image editing task that aims at modifying actions, poses, and interactions of subjects and objects in images instead of just static features like color.
You can use huggingface datasets to read our dataset:
from datasets import load_dataset
dataset = load_dataset("elaine1wan/MotionEdit-Bench")["train"]
vid_motion_mag_eccv18subject_motion
Subject Motion Dataset
A dataset for personalized text-to-video generation, supporting subject customization, motion customization, and subject-motion combination customization.
Dataset Description
Subject Motion Dataset is a images and videos dataset specifically designed for personalized text-to-video generation tasks. The dataset consists of two main components:
Subject: 16 different subjects, each containing 4-6 high-quality images
Motion: 10 different motion videos… See the full description on the dataset page: https://huggingface.co/datasets/Minusone/subject_motion.MotionBooth
Dataset Card for MotionBooth
Dataset Summary
Subject images in MotionBooth are collected from DreamBooth and CustomDiffusion.
@article{wu2024motionbooth,
title={MotionBooth: Motion-Aware Customized Text-to-Video Generation},
author={Jianzong Wu and Xiangtai Li and Yanhong Zeng and Jiangning Zhang and Qianyu Zhou and Yining Li and Yunhai Tong and Kai Chen},
journal={arXiv pre-print arXiv:2406.17758},
year={2024},
}
ImageNet-C-motion_blur-severity_4motionatlas-bench
MotionAtlas-Bench v1
MotionAtlas-Bench v1 is a video multiple-choice benchmark for motion and target-entity understanding. This public release contains MCQ records, answer keys, media, and target-object masks needed to reproduce the visual grounding settings.
Resources
Paper: https://arxiv.org/abs/2606.29531
Project page: https://kagura-0001.github.io/projects/MotionAtlas/
Code: https://github.com/Kagura-0001/MotionAtlas
MotionAtlas-Data:… See the full description on the dataset page: https://huggingface.co/datasets/maxLWSv2/motionatlas-bench.mateiral-controlled-motion-dataset
Motion Estimation Datasets
This repository contains multiple datasets for motion estimation used in the paper"Machine Learning Modeling for Multi-order Human Visual Motion Perception".
Citation
For use, please cite:
@misc{sun2025machinelearningmodelingmultiorder,
title={Machine Learning Modeling for Multi-order Human Visual Motion Processing},
author={Zitang Sun and Yen-Ju Chen and Yung-Hao Yang and Yuan Li and Shin'ya Nishida},
year={2025}… See the full description on the dataset page: https://huggingface.co/datasets/sunana/mateiral-controlled-motion-dataset.motion_energy_segmentation_datasetstext-2-video-human-preferences-motion
Human Preferences for AI-Generated Video: Motion Quality
29,283 pairwise human preference labels comparing 4 frontier video generation models on human motion across 3 quality dimensions, collected from 4,349 real annotators via Datapoint AI.
This is the largest publicly available human preference dataset focused specifically on human motion in AI-generated video.
Why This Dataset
Video generation models are improving fast, but evaluating human motion remains… See the full description on the dataset page: https://huggingface.co/datasets/datapointai/text-2-video-human-preferences-motion.motion_prediction
Visualization of Motion Prediction Task Cases Samples
Check dataset samples visualization by viewing Dataset Viewer.
The sampling procedure is guided by the Elo distribution introduced in our method.
Original dataset is validation split of Waymo Open Motion Dataset (WOMD).
samples/origin: 4409/ 44097
License
This repository is licensed under the Apache License 2.0
bop-motion-mcq
BOP-Motion-MCQ — multiple-choice motion questions over dense 6-DoF video
Multiple-choice questions about how objects move, derived exactly from dense
6-DoF (object→camera) pose trajectories rather than guessed from pixels. Each row pairs
a short 6fps video clip with one motion MCQ, its per-second motion trajectory, and the
whole-video aggregated answer. The intended task: watch the clip and pick the motion that
actually happens.
Built with the motion-qa pipeline… See the full description on the dataset page: https://huggingface.co/datasets/livctr/bop-motion-mcq.text-2-video-human-preferences-motion
Human Preferences for AI-Generated Video: Motion Quality
29,283 pairwise human preference labels comparing 4 frontier video generation models on human motion across 3 quality dimensions, collected from 4,349 real annotators via Datapoint AI.
This is the largest publicly available human preference dataset focused specifically on human motion in AI-generated video.
Why This Dataset
Video generation models are improving fast, but evaluating human motion remains… See the full description on the dataset page: https://huggingface.co/datasets/nusdufv/text-2-video-human-preferences-motion.clr_motion_planning_hw_8corruption-motion_blur
Corruption Dataset: Motion_Blur
Dataset Description
This dataset contains corrupted versions of ImageNet-1K images using motion_blur corruption. It is part of the ImageNet-C benchmark for evaluating model robustness to common image corruptions.
Dataset Structure
Train: 1,281,167 corrupted images
Validation: 50,000 corrupted images
Classes: 1000 ImageNet-1K classes
Format: Arrow (Hugging Face Datasets)
Corruption Type: Motion_Blur
Applies motion… See the full description on the dataset page: https://huggingface.co/datasets/MarMaster/corruption-motion_blur.motion-inputsmotion_planning
Visualization of Motion Planning Task Cases Samples
Check dataset samples visualization by viewing Dataset Viewer.
The sampling procedure is guided by the Elo distribution introduced in our method.
Original dataset is offline test (navtest) split of NAVSIM (Data-Driven Non-Reactive Autonomous Vehicle Simulation and Benchmarking) dataset.
samples: 6072
License
This repository is licensed under the Apache License 2.0