README.md

metadata

license: mit
language:
  - en
size_categories:
  - 1K<n<10K

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

Dataset Card for All-Angles Bench

Dataset Description

The dataset presents a comprehensive benchmark consistin--- license: mit language: - en size_categories: - 1K<n<10K

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

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 can download the dataset (downscaled_takes/448) 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.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

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

Usage

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

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.