ch-chenyu/All-Angles-Bench

Seeing from Another Perspective: Evaluating Multi-View Understanding in MLLMs

Dataset Card for All-Angles Bench

Dataset Description

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

• EgoHumans - Egocentric multi-view human activity understanding dataset
• Ego-Exo4D - Large-scale egocentric and exocentric video dataset for multi-person interaction understanding

Direct Usage

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:

$ 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 would get Access ID and Access Key via email. Then follow the steps below to set up access:

$ pip install awscli
$ aws configure

When prompted, enter the following:

AWS Access Key ID [None]: your Access ID
AWS Secret Access Key [None]: your Access Key
Default region name [None]: us-west-2
Default output format [None]: json

Once configured, run the following to download the dataset (downscaled_takes/448) from this page, and then use the preprocessing scripts to extract the corresponding images.

$ 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, run:

$ python All-Angles-Bench/scripts/json2tsv_pair.py --input All-Angles-Bench/data.json

Dataset Structure

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

@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, Ego-Exo4D, VLMEvalKit. Thanks for their wonderful work and data.