---
license: mit
task_categories:
- video-text-to-text
language:
- en
size_categories:
- 1K<n<10K
---
<!-- <div align="center">
  <h1>RTV-Bench: Benchmarking MLLM Continuous Perception, Understanding and Reasoning through Real-Time Video</h1> 
</div> -->

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<!-- ## 🔥 News
* **`2025.05.03`** 🌟 We are happy to release the RTV-Bench.
  
## TODO
- [ ] Release the final label json.
- [ ] Release the evaluation code.
- [ ] Construct a more comprehensive benchmark for real-time video analysis.
- [ ] ···
## 👀 RTV-Bench Overview

We introduce RTV-Bench, a fine-grained benchmark for MLLM real-time video analysis, which contains **552** videos (167.2 hours) and **4,631** high-quality QA pairs. We evaluated leading MLLMs, including proprietary (GPT-4o, Gemini 2.0), open-source offline (Qwen2.5-VL, VideoLLaMA3), and open-source real-time (VITA-1.5, InternLM-XComposer2.5-OmniLive) models. Experiment results show open-source real-time models largely outperform offline ones but still trail top proprietary models. Our analysis also reveals that larger model size or higher frame sampling rates do not significantly boost $\mathcal{RTV}\text{-}Bench$ performance, sometimes causing slight decreases. This underscores the need for better model architectures optimized for video stream processing and long sequences to advance real-time video analysis with MLLMs.  $\mathcal{RTV}\text{-}Bench$ includes three key principles: 
* **Multi-Timestamp Question Answering (MTQA)**, where answers evolve with scene changes; 
* **Hierarchical Question Structure**, combining basic and advanced queries; and
* **Multi-dimensional Evaluation**, assessing the ability of continuous perception, understanding, and reasoning.  -->


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