---
license: cc-by-nc-sa-4.0
language:
- en
task_categories:
- multiple-choice
- visual-question-answering
tags:
- video
- camera-movement
- vision-language
size_categories:
- 1K<n<100K
configs:
- config_name: real
  data_files:
  - split: test
    path: real_video_test.jsonl
- config_name: syn
  data_files:
  - split: test
    path: syn_video_test.jsonl
- config_name: binary
  data_files:
  - split: test
    path: binary_test.jsonl
---

# ACaM-Bench

**ACaM-Bench** is an atomic, multiple-choice benchmark for evaluating whether vision-language models can recognize fine-grained camera movements in real-world and synthetic videos. It covers a two-level cinematographic taxonomy of 17 atomic camera movement classes spanning translations, rotations, focal-length changes, static shots, and object-centric movements.

## Splits

| Split | # Items | Task | Source |
|---|---:|---|---|
| `real`   | 1464 | 4-way multiple choice | Curated real-world clips |
| `syn`    | 1179 | 4-way multiple choice | AI-generated clips (Veo 3.1 fast preview) |
| `binary` | 1510 | Yes/No question | Synthetic clips (balanced 755 Yes / 755 No) |



> **Note on externally-sourced clips (not redistributed).** Some `real` items are
> sourced from other benchmarks and, to respect their original licenses, are **not
> redistributed here**. Filenames in the JSONLs match the originals — download the
> clips from each source and place them under the paths below:
>
> | Source | Items | Place under |
> |---|---:|---|
> | [**CameraBench**](https://huggingface.co/datasets/syCen/CameraBench) | 454 | `real_videos/Camera_Motion_Bench/videos/` |
> | [**ShotBench**](https://huggingface.co/datasets/Vchitect/ShotBench) | 359 | `real_videos/ShotBench/video/` |
> | [**CineTechBench**](https://huggingface.co/datasets/Xinran0906/CineTechBench) | 79 | `real_videos/CineTechBench/dataset/clips/` |
>
> The remaining real clips (FavorBench, MotionBench, self-collected) and all
> synthetic clips are included in the archives.

## Dataset Structure

```
ACaM-Bench/
├── real_video_test.jsonl      # 1464 entries (4-way MCQ, real)
├── syn_video_test.jsonl       # 1179 entries (4-way MCQ, synthetic)
├── binary_test.jsonl          # 1510 entries (Yes/No, synthetic)
├── real_videos.zip            # real-world video files (unzip in place)
├── syn_videos.zip             # synthetic video files, also used by binary (unzip in place)
└── train.zip                  # training videos archive (see "Training data")
```

The video files are distributed as zip archives. Each archive already contains
its top-level folder, so **unzip them in the repo root** and the paths in the
JSONLs (e.g. `real_videos/foo.mp4`, `syn_videos/bar.mp4`) resolve as-is:

```bash
unzip real_videos.zip   # -> real_videos/...
unzip syn_videos.zip    # -> syn_videos/...
```

## Fields

Each line in the JSONL is a JSON object:

| Field | Type | Description |
|---|---|---|
| `image` | string | Relative path to the video file (e.g. `real_videos/foo.mp4`) |
| `camera movement` | list[string] | Ground-truth camera movement label(s) |
| `question` | string | The natural-language question shown to the model |
| `options` | dict | Four answer choices keyed `A`–`D` |
| `correct_answer` | string | Letter of the correct option |
| `source` | string | Origin of the clip |
| `duration` | float | Video duration in seconds (real split only) |

### `binary` split fields

The `binary` split uses a simpler schema (no `options` / `correct_answer`):

| Field | Type | Description |
|---|---|---|
| `image` | string | Relative path to the video file (e.g. `syn_videos/foo.mp4`) |
| `camera_motion` | list[string] | The motion the question asks about |
| `question` | string | A Yes/No question, e.g. "Does the camera perform an arc movement?" |
| `label` | string | Ground-truth answer, `Yes` or `No` |
| `source` | string | Origin of the clip |

## Training data

The training videos are provided as a single archive, `train.zip`. The archive
contains the per-source folders directly (e.g. `DeDopShots/`, …), so **extract it
into a folder named `training_videos/`** to match the relative paths used by the
training annotations:

```bash
# download from the dataset repo, then:
mkdir -p training_videos
unzip train.zip -d training_videos/
# results in training_videos/DeDopShots/..., etc.
```

The accompanying training annotations (`train.json`, released with the code) refer
to videos via paths like `training_videos/<source>/<file>.mp4`, which resolve
once the archive is extracted as above.

> **Note on externally-sourced training data (not redistributed).** To respect
> their original licenses, some training videos are **not redistributed** here —
> `train.zip` excludes them. To use the full training set, obtain the clips from
> each source and place them under the paths below (filenames in `train.json`
> match):
>
> | Source | Place under |
> |---|---|
> | [**CameraBench**](https://huggingface.co/datasets/syCen/CameraBench) | `training_videos/CameraBench_train_videos/` |
> | [**ShotQA**](https://huggingface.co/datasets/Vchitect/ShotQA) | `training_videos/ShotQA_Training/` |