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Datasets:
humanmovevqa
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HumanMoveVQA

Tasks:
Visual Question Answering
Video-Text-to-Text
Modalities:
Text
Video
Formats:
json
Languages:
English
Size:
100K - 1M
Tags:
video
human-motion
3d-pose
multiple-choice
benchmark
Libraries:
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pandas
Polars
License:
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HumanMoveVQA / README.md
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humanmovevqa
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metadata 
license: other
license_name: academic-non-commercial
task_categories:
  - visual-question-answering
  - video-text-to-text
language:
  - en
tags:
  - video
  - human-motion
  - 3d-pose
  - multiple-choice
  - benchmark
size_categories:
  - 100K<n<1M
configs:
  - config_name: default
    data_files:
      - split: train
        path: train.jsonl
      - split: validation
        path: val.jsonl
      - split: test_emdb
        path: test_emdb.jsonl
      - split: test_rich
        path: test_rich.jsonl
      - split: test_egobody
        path: test_egobody.jsonl
      - split: test_3dpw
        path: test_3dpw.jsonl
      - split: test_sloper4d
        path: test_sloper4d.jsonl

HumanMoveVQA

Multiple-choice question answering about human motion in video. Questions are generated from 3D human pose estimated with PromptHMR and captioned with MotionScript.

This is the v6.3 release. It covers five source datasets: EMDB, RICH, EgoBody, 3DPW, and SLOPER4D.

Files

File Rows Source dataset
train.jsonl 86,797 all five
val.jsonl 11,081 all five
test_emdb.jsonl 768 EMDB
test_rich.jsonl 2,218 RICH
test_egobody.jsonl 6,994 EgoBody
test_3dpw.jsonl 1,294 3DPW
test_sloper4d.jsonl 1,008 SLOPER4D

val is the held-out in-domain split used for checkpoint selection. The test_* files are the evaluation benchmark.

Train, val, and test share no videos and no questions, in every source dataset.

Schema

One row per question:

{
  "id": "q1",
  "source_dataset": "EMDB",
  "source_video_id": "00_mvs_a",
  "source_video_start": "0:00",
  "source_video_end": "0:15.875",
  "video_id": "00_mvs_a",
  "data": "P0/00_mvs_a/images/",
  "category": "comparative",
  "question": "In the first quarter of the video, is the person's first move left faster or slower than their first move right?",
  "A": "left",
  "B": "same category",
  "C": "right",
  "D": null,
  "answer": "A. left"
}
Field Meaning
id Row identifier, unique within a file
source_dataset One of EMDB, RICH, EgoBody, 3DPW, SLOPER4D
source_video_id Full recording. Differs from video_id for EgoBody _partNN segments
source_video_start Segment start within the full recording, M:SS.mmm
source_video_end Segment end within the full recording, M:SS.mmm
video_id The video the question was generated from
data Frame directory relative to the dataset root
category Question type, see below
question Question stem
A, B, C, D Options. C and D are null for two-option questions
answer "<letter>. <option text>"

source_video_start and source_video_end are read from the video file duration, not from the caption metadata.

Categories

Category Count (train + val) What it asks
existence 20,891 Whether a motion event occurred
numerical 16,070 How many times an event occurred
comparative 14,021 Which of two directions has greater magnitude
dominant 13,977 Which direction dominates overall
temporal 13,939 When an event occurred, or its speed
trajectory_affordance 10,946 Path shape and what the motion affords
ordering 8,034 The order in which events occurred

Changes in v6.3

  • Adds 3DPW and SLOPER4D. The previous release covered EMDB, RICH, and EgoBody only.
  • Replaces all train, val, and test files with the v6.3 build.
  • Fixes a duplicate-question bug in the previous test_egobody.jsonl, which held 265 repeated (video_id, question) pairs, 34 of them self-contradictory.
  • Recomputes source_video_start and source_video_end for every row.

The previous release is available at the v6.2-legacy tag.

Source datasets

All five are academic, non-commercial use only. Each requires its own request or download step. The videos are not redistributed here; only the questions and answers are.

Dataset Videos (train / val / test) Frame rate Downsample Access
RICH 701 / 83 / 32 30 fps 8 rich.is.tue.mpg.de
EgoBody 300 / 39 / 54 30 fps 4 sanweiliti.github.io/egobody
EMDB 57 / 7 / 11 30 fps 2 emdb.ait.ethz.ch
3DPW 40 / 4 / 15 30 fps 1 3DPW project page
SLOPER4D 13 / 2 / 15 30 fps 1 SLOPER4D project page

Frame rate is the rate at which image frames are assembled into video. Downsample is the factor applied to the source image width and height when rendering the video.

Split properties:

  • EgoBody and SLOPER4D splits are group-disjoint. EgoBody groups by recording, so views of the same recording never straddle a split. SLOPER4D groups by subject, and the benchmark holds three distinct subjects.
  • SLOPER4D clips pass a horizontal alignment gate (XZ RMS <= 2 m against LiDAR GT). Clips failing the gate, and RGB-only clips with no GT, are excluded.
  • 3DPW is 30 fps while the other three original datasets are 24 fps, so 3DPW results are reported separately in the paper.

Video sources and path mapping

The data field points at the frame directory, not the video file. The layout differs per dataset.

EMDB

Request access: https://emdb.ait.ethz.ch/ Fill out the form with an institutional email address. Access is granted after approval.

Download: Once approved, download all partition folders P0, P1, ..., P9.

"data": "P0/07_outdoor_push_ups/images/"
  ->  <EMDB_ROOT>/P0/07_outdoor_push_ups/images/

Partition nesting: Some archives unzip into a doubled folder, so the frames land at <EMDB_ROOT>/P2/P2/<sequence>/images/. Check your local layout and adjust the prefix if needed.

RICH

Request access: https://rich.is.tue.mpg.de/

Download: From the download page, download the JPEG image files for all three splits: train, val, and test. The data field indicates which split a sequence belongs to, so only the relevant split files are strictly required.

"data": "val/Pavallion_003_yoga1/cam_10/"
  ->  <RICH_ROOT>/val/Pavallion_003_yoga1/cam_10/

Train prefix: Some copies of the RICH train split are nested one level deeper, so the frames land at <RICH_ROOT>/train2/train/<scene>/<camera>/. The data field reflects the layout the frames were read from. Adjust the prefix to match your copy.

EgoBody

Request access & download: https://sanweiliti.github.io/egobody/egobody.html Sign the license and download the kinect_color folder, which holds per-recording image frames organised by camera (master, sub_1, sub_2).

"data": "recording_20211004_S19_S06_01/sub_1/"
  ->  <EGOBODY_ROOT>/kinect_color/recording_20211004_S19_S06_01/sub_1/

Split videos: Some entries have a video_id with a _partNN suffix, for example recording_20211004_S19_S06_01_sub1_part02. This means the questions were generated on a temporal segment of the full recording. source_video_id gives the full recording, and source_video_start / source_video_end give the time range within it. All image frames are still found under the same data path.

3DPW

Download: Download the image sequence archive (imageFiles.zip) and extract it.

"data": "courtyard_backpack_00/"
  ->  <3DPW_ROOT>/imageFiles/courtyard_backpack_00/

Clip videos: Some entries have a video_id with a _clipN suffix, for example downtown_bus_00_clip0. The frames live under the unsuffixed sequence directory, so data is downtown_bus_00/.

SLOPER4D

Download: Download the SLOPER4D dataset and extract the RGB clips.

"data": "seq002_football_001/seq002_football_001_c000/"
  ->  <SLOPER4D_ROOT>/clips/seq002_football_001/seq002_football_001_c000/

Loading 

from datasets import load_dataset

ds = load_dataset("humanmovevqa/HumanMoveVQA", "default")
print(ds["test_emdb"][0])

Citation

If you use this dataset, please cite the HumanMoveVQA paper and the source datasets you use. See the paper for the full reference list.

Generation pipeline

The code that builds this dataset is at github.com/humanmovevqa/HumanMove_VQA.