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---
license: mit
language:
- en
size_categories:
- 1K<n<10K
---
<h1>Seeing from Another Perspective: Evaluating Multi-View Understanding in MLLMs</h1>
<a href='https://danielchyeh.github.io/All-Angles-Bench/'><img src='https://img.shields.io/badge/Project-Page-Green'></a>
<a href='https://arxiv.org/pdf/2504.15280'><img src='https://img.shields.io/badge/Paper-PDF-orange'></a>
<a href='https://arxiv.org/abs/2504.15280'><img src='https://img.shields.io/badge/Arxiv-Page-purple'></a>
<a href="https://github.com/Chenyu-Wang567/All-Angles-Bench/tree/main"><img src='https://img.shields.io/badge/Code-Github-red'></a>
# Dataset Card for All-Angles Bench
## Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
The dataset presents a comprehensive benchmark consistin---
license: mit
language:
- en
size_categories:
- 1K<n<10K
---
<h1>Seeing from Another Perspective: Evaluating Multi-View Understanding in MLLMs</h1>
<a href='https://danielchyeh.github.io/All-Angles-Bench/'><img src='https://img.shields.io/badge/Project-Page-Green'></a>
<a href='https://arxiv.org/pdf/2504.15280'><img src='https://img.shields.io/badge/Paper-PDF-orange'></a>
<a href='https://arxiv.org/abs/2504.15280'><img src='https://img.shields.io/badge/Arxiv-Page-purple'></a>
<a href="https://github.com/Chenyu-Wang567/All-Angles-Bench/tree/main"><img src='https://img.shields.io/badge/Code-Github-red'></a>
# Dataset Card for All-Angles Bench
## Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
The dataset presents a comprehensive benchmark consisting of over 2,100 human-annotated multi-view question-answer (QA) pairs, spanning 90 real-world scenes. Each scene is captured from multiple viewpoints, providing diverse perspectives and context for the associated questions.
## Dataset Sources
<!-- Provide the basic links for the dataset. -->
- **[EgoHumans](https://github.com/rawalkhirodkar/egohumans)** - Egocentric multi-view human activity understanding dataset
- **[Ego-Exo4D](https://github.com/facebookresearch/Ego4d)** - Large-scale egocentric and exocentric video dataset for multi-person interaction understanding
## Usage
```python
from datasets import load_dataset
dataset = load_dataset("ch-chenyu/All-Angles-Bench")
```
## Prepare Full Benchmark Data on Local Machine
1. **Set up Git lfs and clone the benchmark:**
```bash
$ conda install git-lfs
$ git lfs install
$ git lfs clone https://huggingface.co/datasets/ch-chenyu/All-Angles-Bench
```
2. **Download Ego4D-Exo dataset and extract the frames for the benchmark scenes:**
We provide the image files for the EgoHumans dataset. For the Ego-Exo4D dataset, due to licensing restrictions, you will need to first sign the license agreement from the official Ego-Exo4D repository at https://ego4ddataset.com/egoexo-license/. After signing the license, you can download the dataset (`downscaled_takes/448`) and then use the preprocessing scripts to extract the corresponding images.
```bash
$ pip install ego4d --upgrade
$ egoexo -o All-Angles-Bench/ --parts downscaled_takes/448
$ python All-Angles-Bench/scripts/process_ego4d_exo.py --input All-Angles-Bench
```
3. **Transform JSON metadata into benchmark TSV format:**
To convert the metadata from JSON format into a structured TSV format compatible with benchmark evaluation scripts in [VLMEvalKit](https://github.com/open-compass/VLMEvalKit), run:
```bash
$ python All-Angles-Bench/scripts/json2tsv_pair.py --input All-Angles-Bench/data.json
```
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
The JSON data contains the following key-value pairs:
| Key | Type | Description |
|------------------|------------|-----------------------------------------------------------------------------|
| `index` | Integer | Unique identifier for the data entry (e.g. `1221`) |
| `folder` | String | Directory name where the scene is stored (e.g. `"05_volleyball"`) |
| `category` | String | Task category (e.g. `"counting"`) |
| `pair_idx` | String | Index of a corresponding paired question (if applicable) |
| `image_path` | List | Array of input image paths |
| `question` | String | Natural language query about the scene |
| `A`/`B`/`C` | String | Multiple choice options |
| `answer` | String | Correct option label (e.g. `"B"`) |
| `sourced_dataset`| String | Source dataset name (e.g. `"EgoHumans"`) |
## Citation
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
```bibtex
@article{yeh2025seeing,
title={Seeing from Another Perspective: Evaluating Multi-View Understanding in MLLMs},
author={Chun-Hsiao Yeh, Chenyu Wang, Shengbang Tong, Ta-Ying Cheng, Ruoyu Wang, Tianzhe Chu, Yuexiang Zhai, Yubei Chen, Shenghua Gao and Yi Ma},
journal={arXiv preprint arXiv:2504.15280},
year={2025}
}
```
## Acknowledgements
You may refer to related work that serves as foundations for our framework and code repository,
[EgoHumans](https://github.com/rawalkhirodkar/egohumans),
[Ego-Exo4D](https://github.com/facebookresearch/Ego4d),
[VLMEvalKit](https://github.com/open-compass/VLMEvalKit).
Thanks for their wonderful work and data.g of over 2,100 human-annotated multi-view question-answer (QA) pairs, spanning 90 real-world scenes. Each scene is captured from multiple viewpoints, providing diverse perspectives and context for the associated questions.
## Dataset Sources
<!-- Provide the basic links for the dataset. -->
- **[EgoHumans](https://github.com/rawalkhirodkar/egohumans)** - Egocentric multi-view human activity understanding dataset
- **[Ego-Exo4D](https://github.com/facebookresearch/Ego4d)** - Large-scale egocentric and exocentric video dataset for multi-person interaction understanding
## Usage
```python
from datasets import load_dataset
dataset = load_dataset("ch-chenyu/All-Angles-Bench")
```
We provide the image files for the EgoHumans dataset. For the Ego-Exo4D dataset, due to licensing restrictions, you will need to first sign the license agreement from the official Ego-Exo4D repository at https://ego4ddataset.com/egoexo-license/. After signing the license, you can download the dataset and then use the preprocessing scripts provided in our GitHub repository to extract the corresponding images.
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
The JSON data contains the following key-value pairs:
| Key | Type | Description |
|------------------|------------|-----------------------------------------------------------------------------|
| `index` | Integer | Unique identifier for the data entry (e.g. `1221`) |
| `folder` | String | Directory name where the scene is stored (e.g. `"05_volleyball"`) |
| `category` | String | Task category (e.g. `"counting"`) |
| `pair_idx` | String | Index of a corresponding paired question (if applicable) |
| `image_path` | List | Array of input image paths |
| `question` | String | Natural language query about the scene |
| `A`/`B`/`C` | String | Multiple choice options |
| `answer` | String | Correct option label (e.g. `"B"`) |
| `sourced_dataset`| String | Source dataset name (e.g. `"EgoHumans"`) |
## Citation
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
```bibtex
@article{yeh2025seeing,
title={Seeing from Another Perspective: Evaluating Multi-View Understanding in MLLMs},
author={Chun-Hsiao Yeh, Chenyu Wang, Shengbang Tong, Ta-Ying Cheng, Ruoyu Wang, Tianzhe Chu, Yuexiang Zhai, Yubei Chen, Shenghua Gao and Yi Ma},
journal={arXiv preprint arXiv:2504.15280},
year={2025}
}
```
## Acknowledgements
You may refer to related work that serves as foundations for our framework and code repository,
[EgoHumans](https://github.com/rawalkhirodkar/egohumans),
[Ego-Exo4D](https://github.com/facebookresearch/Ego4d),
[VLMEvalKit](https://github.com/open-compass/VLMEvalKit).
Thanks for their wonderful work and data. |