---
license: mit
task_categories:
- video-classification
tags:
  - temporal-reasoning
  - video-understanding
  - benchmark
  - vision-language
dataset_info:
  features:
  - name: relative_path
    dtype: string
  - name: file
    struct:
    - name: bytes
      dtype: binary
    - name: path
      dtype: string
  splits:
  - name: train
    num_bytes: 3012200454
    num_examples: 595
  download_size: 3010737092
  dataset_size: 3012200454
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---

# SpookyBench: A Benchmark for Purely Temporal Video Understanding

SpookyBench is a novel benchmark dataset designed to evaluate the ability of video-language models (VLMs) to understand purely temporal patterns, independent of spatial cues. The dataset consists of 451 videos across four categories: Text, Object Images, Dynamic Scenes, and Shapes. Each video appears as random noise in individual frames, but reveals meaningful content (words, objects, etc.) when viewed as a temporal sequence. This design exposes a critical limitation in current VLMs, which often heavily rely on spatial information and struggle to extract meaning from purely temporal sequences.

[Paper: Time Blindness: Why Video-Language Models Can't See What Humans Can?](https://huggingface.co/papers/2505.24867)

[Project Website: https://timeblindness.github.io/](https://timeblindness.github.io/)

The dataset contains 451 videos distributed as follows:

| **Category** | **Total Videos** | **Description** |
|-------------|-----------------|----------------|
| **Text** | 210 (46.6%) | English words encoded through temporal noise patterns |
| **Object Images** | 156 (34.6%) | Single objects encoded using temporal animation |
| **Dynamic Scenes** | 57 (12.6%) | Video depth maps with temporal motion patterns |
| **Shapes** | 28 (6.2%) | Geometric patterns encoded through temporal sequences |
| **Total** | **451** | **Comprehensive temporal understanding evaluation** |

**Download:** You can download the dataset from Hugging Face using the following command:

```bash
wget https://huggingface.co/datasets/timeblindness/spooky-bench/resolve/main/spooky_bench.zip
unzip spooky_bench.zip
```

**License:** MIT License