--- license: cc-by-4.0 task_categories: - visual-question-answering - video-text-to-text language: - en tags: - video-question-answering - scene-understanding - spatio-temporal-reasoning - temporal-reasoning - benchmark - synthe - diagnostic - video - vlm - clevr pretty_name: CycliST size_categories: - 10K/.

L1 – Unicycle _{One cyclic object, 2–3 clutter objects}	L2 – Unicycle-Cluttered _{One cyclic object, 4–9 clutter objects}	L3 – Bicycle _{Two cyclic objects}
L4 – Tricycle _{Three cyclic objects}	L5 – Nightrider _{Adds a scene-wide periodic light cycle}

## Supported Tasks - **Video Question Answering (VQA):** Free-form answers to template-generated questions, evaluated with an LLM judge (Llama3-70B) for robustness to phrasing. - **Scene Understanding / Captioning:** Describing all objects, their attributes, and their cyclic transitions, scored against ground-truth scene graphs (precision / recall / F1). ## Dataset Structure The repository is organized into three top-level folders: | Folder | Contents | | --- | --- | | `videos/` | Rendered Full-HD `.mp4` video sequences (160 frames @ 32 fps). | | `scenes/` | Per-scene ground-truth metadata (JSON): object attributes and the full temporal evolution of positions, scale, rotation, color, and spatial relations. | | `questions/` | Template-generated question–answer pairs (JSON), organized by question template and tier. | ### Splits The dataset is divided into `train`, `validation`, and `test` splits (videos): | Tier | Train | Test | Validation | Total | | --- | ---: | ---: | ---: | ---: | | L1 – Unicycle | 1,500 | 750 | 750 | 3,000 | | L2 – Unicycle-Cluttered | 1,500 | 750 | 750 | 3,000 | | L3 – Bicycle | 1,500 | 750 | 750 | 3,000 | | L4 – Tricycle | 1,540 | 770 | 770 | 3,080 | | L5 – Nightrider | 1,360 | 680 | 680 | 2,720 | | **Total** | **7,400** | **3,700** | **3,700** | **14,800** | ### Difficulty Tiers Each tier changes one aspect of scene complexity: - **L1 – Unicycle:** Exactly one cyclic object with 2–3 clutter objects. Tests fundamental perception of a single periodic motion or attribute change in isolation. - **L2 – Unicycle-Cluttered:** One cyclic object amidst 4–9 clutter objects, raising the difficulty of isolating the relevant entity. - **L3 – Bicycle:** Two cyclic objects (plus 2–3 clutter objects), introducing interacting cycles and relative-phase reasoning. - **L4 – Tricycle:** Three cyclic objects, further escalating spatio-temporal complexity. - **L5 – Nightrider:** Adds a scene-wide periodic **light cycle** (smoothly interpolating between bright and dark) on top of a balanced mix of L1/L3/L4-style scenes. ## Cycle Types Objects evolve via cycle functions that always return to their initial state after one full period: - **Motion cycles** (change position): *linear* (back-and-forth between two points) and *orbiting* (circular trajectory around a center object, which may itself be moving). - **Attribute cycles** (change appearance): *size* (small ↔ large), *color* (continuous hue interpolation), and *orientation* (continuous rotation; omitted for rotation-invariant shapes such as spheres). - **Light cycles** (scene-wide): sinusoidal modulation of light intensity (used in the Nightrider tier). ## Question Categories Questions are produced with a template-based functional program executed over each scene's ground-truth graph, following the CLEVR/CLEVRER tradition and extended with novel temporal and cyclical operators. **Temporal Descriptive** (balanced yes/no, random baseline ≈ 50%), each with an existential (∃, "ever true") and a universal (∀, "always true") quantifier: - **Query** – probe an attribute of a single object over time. - **Compare** – compare an attribute between two objects. - **Relate** – test a spatial relationship between two objects. **Scene Representative** (random baseline ≈ 30% where multi-valued): - **Cyclic** – *orbit* (which object is the orbit center), *orbit direction* (clockwise / counterclockwise), and *initial / transition* attribute values. - **Numeric** – *counting* (number of cyclic objects), *periodicity* (frames per cycle), and *occurrence* (number of completed cycles). The full set of question templates, placeholders, and functional operators is documented in the appendix of the paper. ## Usage The data is distributed as raw video, scene, and question files. Because the splits span multiple per-template JSON files, the easiest way to start is to clone the repository and read the files directly: ```bash # Requires git-lfs (the videos are large: ~15.6 GB total) git lfs install git clone https://huggingface.co/datasets/AIML-TUDA/CycliST ``` Or download a subset with the Hub API: ```python from huggingface_hub import snapshot_download # Download only the question and scene metadata (skip the large video files) snapshot_download( repo_id="AIML-TUDA/CycliST", repo_type="dataset", allow_patterns=["questions/*", "scenes/*"], ) ``` Refer to the [GitHub repository](https://github.com/simon-kohaut/CycliST) for the render pipeline, question-generation scripts, dataset-split definitions, and the LLM-judge calibration scripts used in the paper. ## Data Generation Scenes are generated procedurally and validated incrementally with a backtracking placement mechanism that enforces margins to scene boundaries and between objects across all frames. Valid scenes are rendered in Blender using the Cycles engine for photorealistic, physically based rendering, with keyframed transformations and built-in interpolation. Each scene yields a 1920×1080 video at 32 fps plus a JSON file recording the complete temporal metadata (position, scale, rotation, color over time) and spatial relationships used to generate the VQA tasks. Lighting and camera setup follow the CLEVR configuration with randomized translations for visual diversity. ## Evaluation Because VLMs produce free-form text, answers are scored with an LLM judge (Llama3-70B), calibrated on 100 questions per pipeline. Reported judge alignment with human annotation is 100% for yes/no and numeric answers, 92.6% for attribute answers, and an F1 of 87.6% for object mapping in scene understanding. Indefinite answers are counted as incorrect for accuracy and excluded from Mean Absolute Error (MAE). The paper reports temporal-descriptive accuracy, orbit/transition accuracy, cycle counting (accuracy and MAE), periodicity estimation (MAE), and scene/cycle captioning (precision, recall, F1). ## Limitations - The dataset is **entirely synthetic** and does not capture the full nuance of real-world scenes. - Cycles use **stationary frequencies**; real-world cyclicity is often non-stationary (e.g., varying day–night intervals). - Object **geometry and material are fixed** over time, and **causal events** between objects (e.g., a traffic light affecting vehicle flow) are not modeled. - The experiments characterize benchmark-level performance gaps rather than reverse-engineering individual models' internal failure modes. ## Licensing Released under the [Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/) license. ## Citation ```bibtex @article{kohaut2026cyclist, title = {CycliST: A Video Language Model Benchmark for Reasoning on Cyclical State Transitions}, author = {Simon Kohaut and Daniel Ochs and Shun Zhang and Benedict Flade and Julian Eggert and Kristian Kersting and Devendra Singh Dhami}, journal = {Journal of Data-centric Machine Learning Research}, year = {2026}, url = {https://openreview.net/forum?id=l03g53HUL2} } ``` ## Authors Simon Kohaut¹²\*, Daniel Ochs¹\*, Shun Zhang¹, Benedict Flade³, Julian Eggert³, Kristian Kersting¹, Devendra Singh Dhami⁴ (\*equal contribution) - ¹ Artificial Intelligence and Machine Learning Lab, TU Darmstadt - ² Konrad Zuse School of Excellence in Learning and Intelligent Systems (ELIZA) - ³ Honda Research Institute Europe GmbH - ⁴ Uncertainty in Artificial Intelligence Group, TU Eindhoven