🎯 EPIC-Bench: A Perception-Centric Benchmark for Fine-Grained Embodied Visual Grounding in Vision-Language Models

📃 Overview

📚 EPIC-Bench is a Mask-Grounding-based benchmark designed to evaluate a VLM’s Visual Perception capability in Embodied Scenarios. EPIC-Bench covers 3 High-Level Categories and 23 Task Types, following the realistic Embodied Workflow:

• 🎯 TargetLocalization: Pinpoint the right object in the scene from a natural-language instruction.
• 🧭 Navigation: Approach the target step by step by reading key visual cues along the way.
• 🤲 Manipulation: Operate on the target through fine-grained, action-oriented Grounded Perception.

Example visualization

For more, download the dataset to explore the full benchmark locally.

Download

Note: The files in the data/ directory are included solely to enable previewing examples in the Hugging Face Dataset Viewer. They are not part of the recommended usage pipeline, and users may safely ignore them when using EPIC-Bench.

EPIC-Bench contains ~35k small files across three task categories. To work around HuggingFace's per-file rate limit and to keep download speed reasonable, the annotations are distributed as three task-level tarballs instead of raw folders.

Download the tarballs from HuggingFace (or ModelScope):

Archive	Size	# Files	Contents
Manipulation.tar.gz	1.70 GB	7,061	AffordanceRegion / ContactRelationship / PlacementRegion
Navigation.tar.gz	2.71 GB	10,868	FeasiblePath / GroundDetection / VisualMatching
TargetLocalization.tar.gz	3.34 GB	17,665	BasicAttributes / EmbodiedCompositionalAttributes / SpatialRelatedAttributes

mkdir -p dataset/annotation/EPIC_Bench
cd dataset/annotation/EPIC_Bench

# Option 1: huggingface-cli
hf download rxc205/EPIC-Bench \
    Manipulation.tar.gz Navigation.tar.gz TargetLocalization.tar.gz \
    --repo-type dataset --local-dir .

# Option 2: modelscope
modelscope download \
  --dataset macarich/EPIC-Bench \
  --local_dir .

# Extract all three (preserves the original folder layout)
for f in Manipulation.tar.gz Navigation.tar.gz TargetLocalization.tar.gz; do
    tar -xzf "$f" && rm "$f"
done

🧰 EPIC-Bench Evaluation Toolkit

We provide evaluation code for both open-source VLMs via ms-swift and API-based VLMs via lmms-eval.
For details, please refer to our GitHub repository.

📰 News

• [2026.5.19] 🎉 Our arXiv paper is now available!
• [2026.5.15] 🚀 HuggingFace and ModelScope Dataset are available!
• [2026.5.15] 🚀 Project Page and Evaluation Code are released, the arXiv paper will come soon.

📋 Todo

• Evaluation code for EPIC-Bench
• The EPIC-Bench datasets
• Make the evaluation pipeline compatible with mask outputs

🏆 Leaderboard and Benchmark

Please refer to the EPIC-Bench Homepage for the full leaderboard, dataset downloads, and data examples.

📚 Citation

@misc{shan2026epicbenchperceptioncentricbenchmarkfinegrained,
      title={EPIC-Bench: A Perception-Centric Benchmark for Fine-Grained Embodied Visual Grounding in Vision-Language Models}, 
      author={Haozhe Shan and Xiancong Ren and Han Dong and Haoyuan Shi and Yingji Zhang and Jiayu Hu and Yi Zhang and Yong Dai and Bin Shen and Lizhen Qu and Zenglin Xu and Xiaozhu Ju},
      year={2026},
      eprint={2605.17070},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2605.17070}, 
}

📜 License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

🙏 Acknowledgements

• ms-swift for open-source VLM inference: ms-swift
• lmms-eval for API/closed-source evaluation: lmms-eval