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.