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; together they
contain 25,785 clips.
Paper · Code · Project page · Checkpoints
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:
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
@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}
}