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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}
}
```
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