Publish bop_mcq_questions

LICENSE.md

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+# LICENSE AND ATTRIBUTION — BOP-Motion-MCQ (Adapted Dataset)
+
+## 1. What this dataset is
+
+"BOP-Motion-MCQ" is a **motion-question derivative** built on top of three existing
+6-DoF object-pose datasets. The **new material** created here — the multiple-choice
+motion questions, the per-second motion trajectories, the whole-video aggregations, and
+all accompanying metadata — is released under the adaptation-layer license in Section 4.
+The **video frames** are re-encoded (temporally resampled to 6 fps, transcoded to JPEG,
+and downscaled) copies of the source datasets' frames; they remain governed by their
+origin licenses in Section 3.
+
+Because the strictest terms present across the sources are **NonCommercial + ShareAlike +
+Attribution** (from HOPE-Video, below), the collection as a whole must be treated as
+**non-commercial, research-only**, and any redistribution of the ShareAlike-obligated
+material must be under **CC BY-NC-SA 4.0** or a compatible license. **Commercial use is
+prohibited.**
+
+---
+
+## 2. Per-source licensing (the corpus is mixed-provenance)
+
+Each source keeps its origin license, inherited unchanged by this derivative. This
+derivative does NOT relicense any source frames.
+
+| `source` | Origin dataset | License | Terms carried forward |
+|---|---|---|---|
+| `ycbineoat` | **YCBInEOAT** — Wen et al., *se(3)-TrackNet*, IROS 2020 | Code repo is **BSD 3-Clause**; the dataset itself carries no separate license grant beyond research release | Attribution (cite the paper); no explicit dataset license — treated as **research-only** here, pending confirmation with the authors |
+| `hope_video` | **HOPE-Video** — Tyree et al. (NVIDIA), IROS 2022 | **CC BY-NC-SA 4.0** | Attribution + **NonCommercial** + **ShareAlike** |
+| `bop_ycbv` | **YCB-Video** (Xiang et al., *PoseCNN*, RSS 2018), as redistributed by the **BOP** benchmark (`ycbv`) | **MIT License** (BOP `ycbv` distribution) | Attribution / copyright-notice retention |
+
+The HOPE-Video **NonCommercial + ShareAlike** terms are the strictest and therefore
+govern the collection as a whole. **BOP-HOPE is excluded** from this dataset: its BOP
+*test* split ships no pose ground truth, so no motion could be derived from it. (The
+`hope_video` source here is the separate *HOPE-Video* release, which does carry per-frame
+camera + object pose ground truth.)
+
+---
+
+## 3. License of the adaptation layer
+
+The **new material** created for this derivative — the motion MCQs, per-second
+trajectories, aggregations, evidence, tables, and manifests — is released under
+**CC BY-NC-SA 4.0**, to honor the ShareAlike obligation inherited from HOPE-Video and to
+keep the collection internally consistent. The underlying source frames remain under their
+respective licenses in Section 2.
+
+---
+
+## 4. Conditions of use (summary)
+
+Anyone using BOP-Motion-MCQ must:
+
+1. Use it for **non-commercial academic research only**.
+2. **Cite** all three underlying datasets (Section 5) as applicable to the `source`(s) used.
+3. **Preserve attribution** and copyright notices for each source.
+4. **Apply ShareAlike** (CC BY-NC-SA 4.0 or compatible) to any redistribution of the
+   HOPE-Video-derived material or of the adaptation layer.
+5. **Honor removal requests** from any original rights holder.
+
+---
+
+## 5. Citations
+
+```bibtex
+@inproceedings{wen2020se3tracknet,
+  title={se(3)-TrackNet: Data-driven 6D Pose Tracking by Calibrating Image Residuals in Synthetic Domains},
+  author={Wen, Bowen and Mitash, Chaitanya and Ren, Baozhang and Bekris, Kostas E.},
+  booktitle={IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
+  year={2020}
+}
+
+@inproceedings{tyree2022hope,
+  title={6-DoF Pose Estimation of Household Objects for Robotic Manipulation: An Accessible Dataset and Benchmark},
+  author={Tyree, Stephen and Tremblay, Jonathan and To, Thang and Cheng, Jia and Mosier, Terry and Smith, Jeffrey and Birchfield, Stan},
+  booktitle={IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
+  year={2022}
+}
+
+@inproceedings{xiang2018posecnn,
+  title={PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes},
+  author={Xiang, Yu and Schmidt, Tanner and Narayanan, Venkatraman and Fox, Dieter},
+  booktitle={Robotics: Science and Systems (RSS)},
+  year={2018}
+}
+
+@inproceedings{hodan2024bop,
+  title={BOP: Benchmark for 6D Object Pose Estimation},
+  author={Hoda{\v{n}}, Tom{\'a}{\v{s}} and others},
+  note={https://bop.felk.cvut.cz},
+  year={2024}
+}
+```
+
+Sources: YCBInEOAT <https://github.com/wenbowen123/iros20-6d-pose-tracking> ·
+HOPE-Video <https://github.com/swtyree/hope-dataset> ·
+YCB-Video / BOP `ycbv` <https://bop.felk.cvut.cz/datasets/>.
+
+---
+
+## 6. Disclaimer
+
+This document is provided to support proper attribution and license compliance for a
+non-commercial research derivative. It is not legal advice. The maintainer is responsible
+for ensuring their specific use complies with all applicable licenses. Where a source's
+exact terms could not be fully resolved (notably the YCBInEOAT dataset grant), the
+strictest applicable terms are assumed until confirmed with the original authors.

README.md

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+---
+license: other
+license_name: bop-motion-mcq-mixed-provenance
+license_link: LICENSE.md
+task_categories:
+- visual-question-answering
+- video-classification
+language:
+- en
+tags:
+- video
+- motion
+- 6dof
+- temporal-reasoning
+- multiple-choice
+- object-motion
+pretty_name: BOP-Motion-MCQ (6-DoF motion questions)
+size_categories:
+- n<1K
+configs:
+- config_name: default
+  data_files:
+  - split: val
+    path: val/metadata.parquet
+---
+
+# 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`](https://github.com/dherrero12/motion-qa) pipeline
+(`motion_qa.datagen.bop_mcq_questions`).
+
+## The four question types
+
+| `qa_type` | answer space | derived from |
+|---|---|---|
+| `motion_direction` | left / right · up / down · toward / away | Δtranslation of one object |
+| `rotation_spin` | clockwise / counter-clockwise | angular-velocity axis vs. the camera |
+| `speed` | faster / slower · speeding up / slowing down | \|velocity\| and its trend |
+| `relative_motion` | approaching / receding | two objects (or object vs. camera) |
+
+Every question always includes an explicit **"no consistent ⟨motion⟩"** option.
+
+## How the answer is derived (two-step, noise-guarded)
+
+1. **Per-second trajectory.** The 6-DoF track is resampled to **6 fps**, swept with
+   sliding 1-second windows (step 1 frame), and each window yields an instantaneous
+   motion signal (direction axis / spin sign / speed / inter-object distance). Windows
+   below an **adaptive noise floor** (a fraction of a high percentile of the track's own
+   magnitude distribution — not a hand-tuned threshold) are marked inactive. Windows are
+   binned into 1-second labels: the `per_second` list **is** the motion story.
+2. **Whole-video answer with an anti-overfit guard.** The per-second labels are
+   aggregated, but the answer is only *solidified* (`decided = true`) when **both** gates
+   pass: the dominant label is supported by at least `min_observations` active bins
+   (default 2) **and** accounts for more than `dominance_threshold` (default 80%) of the
+   active bins. Otherwise the answer is the explicit **"no consistent …"** option
+   (`decided = false`). The `aggregation` struct records `dominant`, `dominant_frac`,
+   `n_active`, `n_supporting`, and both gate settings.
+
+## The three sources (all 6-DoF pose GT)
+
+| `source` | motion | timing | notes |
+|---|---|---|---|
+| `ycbineoat` | **object moves**, camera static | real seconds (~30fps → 6fps) | single YCB object per sequence — so **no `relative_motion`** here |
+| `hope_video` | **camera moves** over a static multi-object tabletop | frame-index / estimated `fps_native` | multi-object; motion is camera-perspective parallax |
+| `bop_ycbv` | **camera moves**, objects static | **sparse, irregular BOP19 keyframes** | timing is **ordinal / approximate**; windows with undefined or too-large Δt are skipped — the row/evidence flags this honestly |
+
+Per-source caveats to keep in mind:
+
+- **`ycbineoat`** is the only source where motion is literally the object's own
+  translation/rotation; the other two are camera-perspective.
+- **`bop_ycbv`** frames are irregular keyframes (im_id gaps up to ~900). `t` is not a
+  uniform timeline — spacing is ordinal and timing is approximate; do not read the
+  per-second bins as exact wall-clock seconds for this source.
+- **BOP-HOPE is excluded**: its BOP test split ships **no pose ground truth**, so no
+  motion can be derived. (The `hope_video` source above is the *HOPE-Video* release,
+  which does carry per-frame camera + object poses.)
+
+## What's in the repo
+
+```
+val/metadata.parquet / .jsonl   # the table (load_dataset); per_second + aggregation inline
+val/metadata.csv                # browsable view (heavy per_second/evidence dropped)
+frames/<source>__<seq>.zip      # the 6fps JPEG frames (rgb/000000.jpg …), one zip per sequence
+                                #   (+ mask/000000.png where the source ships per-object masks)
+README.md                       # this card
+LICENSE.md                      # full license + attribution (mixed-provenance)
+```
+
+Only sequences that have shipped rows are included, and the frames are **re-encoded to
+JPEG and downscaled** (longest side ≤ 640 px) — the lossless PNG sources are ~100 MB per
+sequence and the model only needs to watch the 6fps video.
+
+## Row schema (`val/metadata.parquet` / `.jsonl`)
+
+One row per Item (one MCQ over one or two tracked objects):
+
+| field | type | meaning |
+|---|---|---|
+| `id` | string | `⟨source⟩/⟨seq⟩/⟨qa_type⟩/⟨obj⟩` (+ `/vs⟨obj2⟩` for relative), unique |
+| `source` | string | `ycbineoat` \| `hope_video` \| `bop_ycbv` |
+| `seq_key` | string | e.g. `bop_ycbv/000048` |
+| `qa_type` | string | `motion_direction` \| `rotation_spin` \| `speed` \| `relative_motion` |
+| `reference_frame` | string | `camera` \| `object_local` \| `relative` |
+| `object_ids` | list[int] | the tracked object slot(s) |
+| `category` | string | object name(s), e.g. `master chef can` |
+| `question` / `options` / `answer_idx` / `answer_text` | string / list / int / string | the MCQ (answer = the aggregated whole-video decision) |
+| `per_second` | string (JSON) | list of `{second,t0,t1,label,active,magnitude,evidence}` — the trajectory |
+| `aggregation` | string (JSON) | `{dominant,dominant_frac,n_active,n_supporting,min_observations,dominance_threshold,decided}` |
+| `n_frames` / `fps` | int / float | resampled clip geometry (`fps` = 6) |
+| `frames_zip` | string | path to this sequence's frame zip in the repo |
+| `corrected` | bool | the auto-derived answer was fixed by a human reviewer |
+| `verified` | bool | human-verified (the publish gate) |
+| `note` | string | reviewer note, if any |
+| `evidence` | string (JSON) | provenance for the derivation (`qa_type`, `timing`, gate stats, trajectory, …) |
+
+`per_second`, `aggregation`, and `evidence` are **JSON-encoded strings** so their nested,
+per-`qa_type`-varying payloads survive parquet's columnar schema — `json.loads` to expand
+them. The CSV view drops `per_second` and `evidence` for browsability.
+
+## Quickstart — `load_dataset`
+
+```python
+import json
+from datasets import load_dataset
+
+ds = load_dataset("livctr/bop-motion-mcq", split="val")
+row = ds[0]
+print(row["question"])
+print(row["options"][row["answer_idx"]])
+
+trajectory = json.loads(row["per_second"])       # per-second motion labels
+agg = json.loads(row["aggregation"])             # decided? dominant? gate stats
+# frames come from frames/<seq_key with '/'→'__'>.zip  (JPEGs rgb/000000.jpg …)
+```
+
+## License & attribution
+
+BOP-Motion-MCQ is **non-commercial, research-only**, and **mixed-provenance**. The
+questions/trajectories/metadata added here are the new material; each source keeps its
+origin license (YCBInEOAT, HOPE-Video, and YCB-Video/BOP). Per-source terms are in
+[`LICENSE.md`](LICENSE.md); use of a source's frames is governed by that source's license.
+Any use must cite the underlying datasets (see `LICENSE.md`).

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