| --- |
| license: cc-by-nc-4.0 |
| pretty_name: CineScript (train split) |
| size_categories: |
| - 10K<n<100K |
| tags: |
| - camera-trajectory |
| - cinematography |
| - text-to-trajectory |
| --- |
| |
| # CineScript — Train Split |
|
|
| The train split of **CineScript**, the dataset introduced in |
| **Unveiling the Value of Motion for Cinematic Camera Trajectories** (NeurIPS 2026). It is the data |
| **CineGEN** was trained on. The matching val split is |
| [`Ziqi1018/CineScript-eval`](https://huggingface.co/datasets/Ziqi1018/CineScript-eval); together they |
| contain 25,785 clips. |
|
|
| [Paper](https://arxiv.org/abs/2609.38683) · [Code](https://github.com/Jia1018/CineGEN) · [Project page](https://jia1018.github.io/CineGEN/) · [Checkpoints](https://huggingface.co/Ziqi1018/CineGen-ckpts) |
|
|
| ## Statistics |
|
|
| Counted on this release. |
|
|
| | | | |
| |---|---| |
| | Clips | 23,207 | |
| | Camera poses | 3,840,301 | |
| | Poses per clip | mean 165.5, median 136, range 44–901 | |
| | Clips linked to movie metadata | 3,232, from 1,233 films | |
|
|
| | Source dataset | `dataset` value | Clips | |
| |---|---|---| |
| | CMD (Condensed Movies) | `condensedmovies` | 18,088 | |
| | CineTechBench | `cinetechbench` | 1,800 | |
| | VADB (film-derived subset) | `vadb` | 1,559 | |
| | MovieShots | `movieshots` | 1,432 | |
| | ShotBench | `shotbench` | 328 | |
|
|
| ## Layout |
|
|
| ``` |
| index.jsonl # one entry per clip |
| matrices.tar.gz # matrices/<clip_id>.npz, camera trajectories (23,207 files) |
| depth.tar.gz # depth/<clip_id>.npy, 128-D depth features (16,892 files) |
| clip_movie_mapping.json # movie attributes for the metadata-linked clips |
| stats.json # the counts above |
| ``` |
|
|
| The trajectories and depth features are shipped as archives because a Hugging Face directory holds at |
| most 10,000 files. After extraction the layout and fields are the same as in `CineScript-eval`, so code |
| written for one reads the other: |
|
|
| ```bash |
| tar -xzf matrices.tar.gz && tar -xzf depth.tar.gz |
| ``` |
|
|
| `./scripts/download.sh --train` in the GitHub repository downloads and extracts the pack in one step. |
|
|
| ## Fields |
|
|
| **`index.jsonl`** |
|
|
| | Field | Content | |
| |---|---| |
| | `clip_id` | Clip identifier: source video ID plus the scene or shot index assigned during processing | |
| | `dataset` | Source dataset, see the table above | |
| | `motion_caption` | Motion caption describing the camera movement | |
| | `logline_script` | Screenplay-style logline, `[INT./EXT.] [Location] - [Time] - [Action]`; empty for 15 clips | |
| | `macro_type`, `setting_class`, `subject_composition`, `genre_vibe` | Additional scene tags; not used to train CineGEN | |
|
|
| **`matrices/<clip_id>.npz`**: `data` is an `(N, 4, 4)` array of camera-to-world matrices estimated with |
| ViPE, `inds` the corresponding source frame indices. The **DirSpeed** and **Pose9D** representations are |
| computed from these matrices by the code in the GitHub repository; CineGEN uses at most 300 steps per |
| clip during training. |
|
|
| **`clip_movie_mapping.json`**: for each metadata-linked clip, the movie title, release year, genres, |
| directors, countries and IMDb ID, retrieved from Wikidata, Wikipedia and IMDb. |
|
|
| ## Split |
|
|
| This is the split used to train CineGEN: all clips are shuffled with seed 42, the first 10% form the |
| val split (`CineScript-eval`) and the rest this train split. The two splits do not overlap. |
|
|
| ## Videos |
|
|
| No video frames are included. `clip_id` identifies the source video (for example the YouTube ID in CMD |
| and ShotBench, or the IMDb ID in MovieShots) followed by the scene or shot index assigned during |
| processing; segment boundaries are not part of this release. The source videos are available from the |
| original datasets under their own terms. |
|
|
| ## License |
|
|
| The annotations in this repository are released under CC BY-NC 4.0. The source videos remain under the |
| licenses of their original datasets. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @inproceedings{zhou2026unveiling, |
| title = {Unveiling the Value of Motion for Cinematic Camera Trajectories}, |
| author = {Zhou, Ziqi and Yuan, Yujian and Sevilla-Lara, Laura}, |
| booktitle = {Advances in Neural Information Processing Systems (NeurIPS)}, |
| year = {2026} |
| } |
| ``` |
|
|
