id stringclasses 5
values | question_text stringclasses 5
values | answer1 stringclasses 5
values | answer2 stringclasses 1
value | reminder1 stringclasses 5
values | reminder2 stringclasses 1
value | video_type stringclasses 4
values | video_duration float64 26 45 | video video 26 45 | question_audio audioduration (s) 26 45 |
|---|---|---|---|---|---|---|---|---|---|
0 | The girl who is playing the violin is wearing black trousers. | The girl who is playing the violin is wearing an orange dress. | 15.0 | Art | 30 | ||||
1 | The person who is speaking in the picture now is the same person as the one who first appeared in the picture to speak. | The person who is speaking in the picture now is not the same person as the one who first appeared in the picture to speak. | 22.0 | News | 45 | ||||
2 | The exterior of this cruise ship sailing in the sea is black. | The exterior of this cruise ship sailing in the sea is white. | 6.0 | Education | 26.911995 | ||||
3 | The woman speaking in the frame isn't wearing glasses right now. | The woman speaking in the frame is wearing glasses right now. | 19.0 | Lifestyle | 30 | ||||
4 | The man who's speaking is wearing a green T-shirt. | The man who's speaking is wearing a yellow T-shirt. | 10.0 | Lifestyle | 25.982993 |
Omni-DuplexEval-Examples is a curated subset of Omni-DuplexEval for qualitative visualization and paper demonstration. Each task contains 5 representative samples, covering all benchmark scenarios and task types.
This subset is intended for illustrative purposes in the paper and supplementary materials. The annotation format and data structure are consistent with the full benchmark.
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.
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