---
license: apache-2.0
---

# Omni-DuplexEval

[**📖 arXiv**](https://arxiv.org/abs/2605.17360) | [**GitHub**](https://github.com/OpenBMB/Omni-DuplexEval)

Omni-DuplexEval is a benchmark for evaluating real-time duplex multimodal interaction. Unlike conventional offline video understanding benchmarks, Omni-DuplexEval focuses on streaming settings where models must continuously process evolving multimodal inputs and decide what to respond and when to respond.

The benchmark contains two scenarios:

* Real-Time Description (RTD): evaluates continuous streaming description ability.
* Proactive Reminder (PR): evaluates event-aware and proactive interaction ability.

Omni-DuplexEval contains 660 samples in total, including 300 samples for Real-Time Description and 360 samples for Proactive Reminder. The dataset consists of 9 tasks with human-curated timestamp-level annotations.

## Benchmark Structure

### Real-Time Description (RTD)

RTD evaluates whether models can continuously describe evolving video content under streaming settings.

Included tasks:

* RTD_Omni
* RTD_counting
* RTD_fine_grained_movement
* RTD_interaction_relation
* RTD_OCR
* RTD_world_knowledge

### Proactive Reminder (PR)

PR evaluates whether models can identify relevant events and respond at the appropriate time.

Included tasks:

* PR_event_reminder
* PR_post_event_reminder
* PR_correction

## Data Format

Each sample contains the following fields:

| Field | Description |
|---|---|
| `id` | Unique sample identifier |
| `video` | Video file |
| `question_audio` | Audio version of the question. The audio duration matches the video duration. |
| `question_text` | Text version of the question |
| `answer1` | Reference answer 1 |
| `answer2` | Reference answer 2 |
| `reminder1` | Timestamp annotation 1 |
| `reminder2` | Timestamp annotation 2 |
| `video_type` | Video category |
| `video_duration` | Video duration |

### Annotation Details

Question Audio

question_audio is aligned with the full video duration.
* For RTD tasks, the question is asked at the beginning of the video.
* For PR tasks, the question may occur at arbitrary timestamps.
Reference Answers
* answer1 and answer2 provide human-annotated reference responses.
* In correction tasks, answer1 additionally contains the corrected target response.
* In PR_event_reminder and PR_post_event_reminder, the answer fields are empty.

Reminder Timestamps

* reminder1 and reminder2 store timestamp annotations related to reminder events.
* In correction tasks, reminder1 contains the timestamp of the incorrect user statement/event.
* In RTD tasks, reminder fields are empty.

## Data Collection and Safety

Videos are collected from publicly accessible platforms such as YouTube and Bilibili. The dataset does not contain personal sensitive information.

During dataset construction:

* RTD videos are selected to contain clear temporal dynamics and continuously evolving subjects.
* PR videos are selected to contain explicit and unambiguous events for stable evaluation.

All samples undergo manual inspection, and potentially unsafe or high-risk content is excluded.

## Citation

If you do find our code helpful or use our benchmark dataset, please citing our paper.

**BibTeX:**
```bibtex
@misc{he2026omniduplexevalevaluatingrealtimeduplex,
      title={Omni-DuplexEval: Evaluating Real-time Duplex Omni-modal Interaction}, 
      author={Chaoqun He and Mingyang Xiang and Yingjing Xu and Bokai Xu and Junbo Cui and Jie Zhou and Yuan Yao and Lijie Wen},
      year={2026},
      eprint={2605.17360},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2605.17360}, 
}
```