MoveVQA v6.3: five datasets, recomputed timing

README.md

@@ -1,245 +1,86 @@
----
-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
+# Video Sources
+
+This document explains how to obtain the source videos for each dataset used in this benchmark.
+The `data` field in each JSONL entry points to the relative path of the image frames within the dataset's directory structure.
+
+**Frame rate:** All datasets use **24 fps** when assembling image frames into video. The `source_video_start` and `source_video_end` timestamps are computed at this frame rate.
+
+**Spatial downsampling** (width/height divided by factor when converting frames to video):
+
+| Dataset | Downsample factor |
+|---------|------------------|
+| EMDB | 4 |
+| RICH | 8 |
+| EgoBody | 4 |
+
 ---
 
-# HumanMoveVQA
-
-Multiple-choice question answering about human motion in video. Questions are
-generated from 3D human pose estimated with
-[PromptHMR](https://github.com/yufu-wang/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:
-
-```json
-{
-  "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"
-}
-```
+## EMDB
 
-| 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](https://rich.is.tue.mpg.de/) |
-| EgoBody | 300 / 39 / 54 | 30 fps | 4 | [sanweiliti.github.io/egobody](https://sanweiliti.github.io/egobody/egobody.html) |
-| EMDB | 57 / 7 / 11 | 30 fps | 2 | [emdb.ait.ethz.ch](https://emdb.ait.ethz.ch/) |
-| 3DPW | 40 / 4 / 15 | 30 fps | 1 | [3DPW project page](https://virtualhumans.mpi-inf.mpg.de/3DPW/) |
-| SLOPER4D | 13 / 2 / 15 | 30 fps | 1 | [SLOPER4D project page](https://github.com/climbingdaily/SLOPER4D) |
-
-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
+**License:** Academic / non-commercial use only. Requires institutional approval.
 
 **Request access:** [https://emdb.ait.ethz.ch/](https://emdb.ait.ethz.ch/)
-Fill out the form with an institutional email address. Access is granted after approval.
+Fill out the form with an institutional email address. Access to the download is granted after approval.
 
 **Download:** Once approved, download all partition folders `P0`, `P1`, ..., `P9`.
 
+**Data path mapping:**
+The `data` field gives the path to the image frames relative to the EMDB root, e.g.:
+
 ```
 "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/](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.
+corresponds to:
 
 ```
-"data": "val/Pavallion_003_yoga1/cam_10/"
-  ->  <RICH_ROOT>/val/Pavallion_003_yoga1/cam_10/
+<EMDB_ROOT>/P0/07_outdoor_push_ups/images/
 ```
 
-> **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](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`).
+## RICH
 
-```
-"data": "recording_20211004_S19_S06_01/sub_1/"
-  ->  <EGOBODY_ROOT>/kinect_color/recording_20211004_S19_S06_01/sub_1/
-```
+**License:** Academic / non-commercial use only.
 
-> **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.
+**Request access:** [https://rich.is.tue.mpg.de/](https://rich.is.tue.mpg.de/)
 
-### 3DPW
+**Download:** From the download page, download the JPEG image files for all three splits: `train`, `val`, and `test`. The `data` field in each entry indicates which split the sequence belongs to, so only the relevant split files are strictly required for a given JSONL.
 
-**Download:** Download the image sequence archive (`imageFiles.zip`) and extract it.
+**Data path mapping:**
+The `data` field gives `<split>/<scene>/<camera>/`, e.g.:
 
 ```
-"data": "courtyard_backpack_00/"
-  ->  <3DPW_ROOT>/imageFiles/courtyard_backpack_00/
+"data": "val/Pavallion_003_yoga1/cam_10/"
 ```
 
-> **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.
+corresponds to:
 
 ```
-"data": "seq002_football_001/seq002_football_001_c000/"
-  ->  <SLOPER4D_ROOT>/clips/seq002_football_001/seq002_football_001_c000/
+<RICH_ROOT>/val/Pavallion_003_yoga1/cam_10/
 ```
 
-## Loading
+---
 
-```python
-from datasets import load_dataset
+## EgoBody
 
-ds = load_dataset("humanmovevqa/HumanMoveVQA", "default")
-print(ds["test_emdb"][0])
-```
+**License:** Academic / non-commercial use only.
 
-## Citation
+**Request access & Download:** [https://sanweiliti.github.io/egobody/egobody.html](https://sanweiliti.github.io/egobody/egobody.html)
+Sign the license and download the `kinect_color` folder, which contains per-recording image frames organised by camera (`master`, `sub_1`, `sub_2`).
 
-If you use this dataset, please cite the HumanMoveVQA paper and the source datasets
-you use. See the paper for the full reference list.
+**Data path mapping:**
+The `data` field gives `<recording>/<camera>/`, e.g.:
 
-## Generation pipeline
+```
+"data": "recording_20211004_S19_S06_01/sub_1/"
+```
+
+corresponds to:
+
+```
+<EGOBODY_ROOT>/kinect_color/recording_20211004_S19_S06_01/sub_1/
+```
 
-The code that builds this dataset is at
-[github.com/humanmovevqa/HumanMove_VQA](https://github.com/humanmovevqa/HumanMove_VQA).
+> **Note on split videos:** Some entries have a `video_id` with a `_partXX` suffix (e.g. `recording_20211004_S19_S06_01_sub1_part02`). This indicates the QA was generated on a temporal segment of the full recording. The `source_video_start` and `source_video_end` fields give the time range within the full recording. All image frames are still found under the same `data` path.

train.jsonl

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