| --- |
| license: cc-by-4.0 |
| pretty_name: MotionBlind |
| language: |
| - en |
| task_categories: |
| - visual-question-answering |
| - video-text-to-text |
| tags: |
| - video |
| - video-llm |
| - motion-understanding |
| - physical-reasoning |
| - contrastive |
| - minimal-pairs |
| - benchmark |
| size_categories: |
| - n<1K |
| configs: |
| - config_name: default |
| data_files: |
| - split: test |
| path: videos/** |
| - config_name: annotations |
| data_files: |
| - split: test |
| path: data/motionblind.jsonl |
| --- |
| |
| # MotionBlind |
|
|
| **A contrastive benchmark for physical-motion perception in Video-LLMs.** |
|
|
| [](https://arxiv.org/abs/2609.09528) |
|
|
| 📄 **Paper:** [MotionBlind: Probing the Illusion of Motion Understanding in Video-LLMs](https://arxiv.org/abs/2609.09528) |
|
|
| A Video-LLM can watch two clips of the same person in the same room and name every |
| object in both, yet fail to tell you which one moves *faster*, which way a hand travels, |
| or how *far* a box slides. **MotionBlind** is a contrastive, minimal-pairs benchmark |
| built to expose exactly that gap: pairs of near-identical clips that differ **only** in |
| motion, each paired with two complementary yes/no questions. |
|
|
| <p align="center"> |
| <img src="assets/overview.png" width="90%"> |
| </p> |
|
|
| ## Why it is hard |
|
|
| Each instance is a **2×2**: two near-identical videos (\(i_0, i_1\)) crossed with two |
| complementary questions (\(q_0, q_1\)), with a **diagonal** gold pattern |
| (\(q_0\) is *yes* for \(i_0\) and *no* for \(i_1\); vice-versa for \(q_1\)). Because the |
| paired clips share static content and differ only in motion, a model that ignores the |
| temporal signal cannot satisfy the pattern no matter how good its object recognition is. |
| Single-frame, appearance, and language-only shortcuts all collapse toward the **6.25 %** |
| chance floor. |
|
|
| ## What is inside |
|
|
| | | | |
| |---|---| |
| | Instances (2×2) | **41** | |
| | Clips | **82** | |
| | Items (video × question) | **164** | |
| | Answer balance | 50 / 50 yes / no | |
| | Modality | short self-recorded video + text | |
| | Language | English | |
|
|
| All categories are **physical / kinematic** — precisely the variables that are hard to |
| source and label from uncontrolled internet video, but directly controllable in a lab: |
|
|
| | Characteristic | Subtype | Instances | Example question | |
| |---|---|---|---| |
| | Speed | successive | 6 | *Does the person stir faster the first time than the second?* | |
| | Magnitude | successive | 15 | *Does the toy car travel farther in the second push than the first?* | |
| | Direction | translational | 14 | *From the camera's point of view, is the man walking to the right?* | |
| | Direction | rotational | 6 | *Is the bottle spun clockwise?* | |
|
|
| ## Metric |
|
|
| We use the TimeBlind / Winoground-style nested scores (primary = **Instance Accuracy**): |
|
|
| - **Acc** — fraction of individual items correct (chance 50 %). |
| - **Q_Acc** — a question scores only if correct on *both* videos (chance 25 %). |
| - **V_Acc** — a video scores only if *both* its questions are correct (chance 25 %). |
| - **I_Acc** — an instance scores only if **all four** items are correct (chance **6.25 %**). |
| |
| ### Human ceiling vs. models |
| |
| | | Acc | I_Acc | |
| |---|---|---| |
| | **Human** (mean of 5 annotators) | **97.8** | **91.3** | |
| | Best open Video-LLM (≤ 8B) | ~60 | **≤ 25** | |
| | Chance | 50.0 | 6.25 | |
|
|
| Per-item accuracy looks competent, but instance-level discrimination sits near the floor |
| — the illusion the benchmark is named for. See the [paper](https://arxiv.org/abs/2609.09528) |
| for the full study of open and frontier Video-LLMs. |
|
|
| ## Dataset structure |
|
|
| The **default** viewer config is a videofolder — each row is a clip with an inline |
| player plus its two complementary questions (`videos/metadata.jsonl`): |
|
|
| ```json |
| {"file_name": "02_00_0.mp4", "instance_id": "02_00", "category": "Speed", |
| "subtype": "successive", "variant": 0, |
| "question_1": "Does the person stir faster the first time compared to the second time?", |
| "question_2": "Does the person stir slower the first time compared to the second time?"} |
| ``` |
|
|
| The `annotations` config exposes the flat item view — `data/motionblind.jsonl`, |
| one row per item (164 rows): |
|
|
| ```json |
| {"instance_id": "02_00", "category": "Speed", "subtype": "successive", |
| "video": "02_00_0.mp4", "variant": 0, "question_idx": 1, |
| "question": "Does the person stir faster the first time compared to the second time?"} |
| ``` |
|
|
| `data/instances.jsonl` — one row per 2×2 instance (41 rows), with `video_0/1` and |
| `question_1/2`. Videos live in `videos/`. |
|
|
| **Gold labels.** Ground-truth answers are withheld from this release for now. Files are |
| named `<catID>_<pair>_<variant>.<ext>`; the scoring follows the diagonal minimal-pairs |
| pattern described above. |
|
|
| ## Usage |
|
|
| ```python |
| from datasets import load_dataset |
| from huggingface_hub import snapshot_download |
| |
| # default config: clips with an inline video column + the two questions |
| ds = load_dataset("augmentedcognitionlab/MotionBlind", split="test") |
| ds[0]["video"] # decoded video |
| ds[0]["question_1"] # first question |
| |
| # flat item view (one row per video x question) |
| items = load_dataset("augmentedcognitionlab/MotionBlind", "annotations", split="test") |
| |
| # raw files on disk |
| path = snapshot_download("augmentedcognitionlab/MotionBlind", repo_type="dataset") |
| ``` |
|
|
| ## License & intended use |
|
|
| Released under CC-BY-4.0 for research on motion perception in video models. Human faces |
| in all clips are automatically blurred. Please use responsibly. |
|
|
| ## Citation |
|
|
| If you use MotionBlind, please cite: |
|
|
| ```bibtex |
| @article{bhatia2026motionblind, |
| title = {MotionBlind: Probing the Illusion of Motion Understanding in Video-LLMs}, |
| author = {Bhatia, Dhairya and Galoaa, Bishoy and Fritsche, Oliver and Kamal, Shahid and |
| Salam, Muhammad Obaidullah Abdul and Saleem, Umer and Rastogi, Om and |
| Chettiar, Frania Felix and Erdogmus, Nesli and Ostadabbas, Sarah}, |
| journal = {arXiv preprint arXiv:2609.09528}, |
| year = {2026}, |
| url = {https://arxiv.org/abs/2609.09528} |
| } |
| ``` |
|
|
| Maintained by the Augmented Cognition Lab (ACLab), Northeastern University. |
