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| license: apache-2.0 |
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| # Omni-DuplexEval |
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| [**📖 arXiv**](https://arxiv.org/abs/2605.17360) | [**GitHub**](https://github.com/OpenBMB/Omni-DuplexEval) |
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| 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. |
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| The benchmark contains two scenarios: |
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| * Real-Time Description (RTD): evaluates continuous streaming description ability. |
| * Proactive Reminder (PR): evaluates event-aware and proactive interaction ability. |
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| 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. |
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| ## Benchmark Structure |
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| ### Real-Time Description (RTD) |
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| RTD evaluates whether models can continuously describe evolving video content under streaming settings. |
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| Included tasks: |
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| * RTD_Omni |
| * RTD_counting |
| * RTD_fine_grained_movement |
| * RTD_interaction_relation |
| * RTD_OCR |
| * RTD_world_knowledge |
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| ### Proactive Reminder (PR) |
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| PR evaluates whether models can identify relevant events and respond at the appropriate time. |
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| Included tasks: |
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| * PR_event_reminder |
| * PR_post_event_reminder |
| * PR_correction |
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| ## Data Format |
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| Each sample contains the following fields: |
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| | 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 | |
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| ### Annotation Details |
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| Question Audio |
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| 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. |
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| Reminder Timestamps |
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| * 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. |
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| ## Data Collection and Safety |
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| Videos are collected from publicly accessible platforms such as YouTube and Bilibili. The dataset does not contain personal sensitive information. |
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| During dataset construction: |
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| * 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. |
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| All samples undergo manual inspection, and potentially unsafe or high-risk content is excluded. |
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| ## Citation |
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| If you do find our code helpful or use our benchmark dataset, please citing our paper. |
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| **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}, |
| } |
| ``` |
