StreamAV-Bench: A Comprehensive Benchmark for Streaming Audio-Video Generation
- URL: http://arxiv.org/abs/2608.26336v1
- Date: Wed, 26 Aug 2026 19:16:24 GMT
- Title: StreamAV-Bench: A Comprehensive Benchmark for Streaming Audio-Video Generation
- Abstract summary: StreamAV-Bench is the first comprehensive benchmark tailored for streaming audio-video generation.<n>Our analysis reveals that current models suffer from temporal drift in progressive generation and responsiveness bottlenecks during interactive control.<n>Based on a comprehensive failure analysis, we share insights to advance the development of native joint audio-video streaming models.
- Score: 48.448082116311156
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recent advancements in generative models are pushing video generation toward unbounded streaming audio-video generation for real-time interactive worlds. However, existing benchmarks primarily evaluate completed sequences and struggle to capture streaming properties. To bridge this gap, we introduce StreamAV-Bench, the first comprehensive benchmark tailored for streaming audio-video generation. StreamAV-Bench establishes a unified evaluation framework, including the progressive track for instruction adherence and long-horizon stability, and the interactive track for interactive response and state retention and reuse. With expert-verified evaluation cases across 32 fine-grained dimensions, we conduct an extensive evaluation of 13 representative systems. Our analysis reveals that current models suffer from temporal drift in progressive generation and responsiveness bottlenecks during interactive control. Based on a comprehensive failure analysis, we share insights to advance the development of native joint audio-video streaming models.
Related papers
- DiVA: Enabling Interactive Digital Life Simulation via Video Models [107.640138694388]
DiVA is an interactive digital life simulator pioneering a new paradigm for long-term, open-ended interactive experiences.<n>It maintains long-term visual quality and realism, validating its effectiveness as a sustainable, interactive simulation.
arXiv Detail & Related papers (2026-09-12T09:33:15Z) - MSAVBench: Towards Comprehensive and Reliable Evaluation of Multi-Shot Audio-Video Generation [88.7702943548674]
We introduce MSAVBench, the first comprehensive benchmark and adaptive hybrid evaluation framework for multi-shot audio-video generation.<n>Our benchmark spans four key dimensions, video, audio, shot, and reference, covering diverse task settings, varying shot counts of up to 15, and challenging non-realistic scenarios.<n> MSAVBench achieves high alignment with human judgments, reaching a Spearman rank correlation of 91.5%.
arXiv Detail & Related papers (2026-05-19T17:59:33Z) - Stream-T1: Test-Time Scaling for Streaming Video Generation [30.575612184373025]
Stream-T1 is a pioneering comprehensive Test-Time Scaling framework exclusively tailored for streaming video generation.<n> evaluated on both 5s and 30s comprehensive video benchmarks, Stream-T1 demonstrates profound superiority.
arXiv Detail & Related papers (2026-05-06T03:40:05Z) - AVGen-Bench: A Task-Driven Benchmark for Multi-Granular Evaluation of Text-to-Audio-Video Generation [42.157453071865056]
AVGen-Bench is a task-driven benchmark for T2AV generation featuring high-quality prompts across 11 real-world categories.<n>We propose a multi-granular evaluation framework that combines lightweight specialist models with Multimodal Large Language Models (MLLMs)<n>Our evaluation reveals a pronounced gap between strong audio-visual aesthetics and weak semantic reliability, including persistent failures in text rendering, speech coherence, physical reasoning, and a universal breakdown in musical pitch control.
arXiv Detail & Related papers (2026-04-09T17:59:39Z) - STRIDE: When to Speak Meets Sequence Denoising for Streaming Video Understanding [77.20037111885226]
Real-world deployments increasingly require streaming perception and proactive interaction.<n>In this work, we revisit proactive activation in streaming video as a structured sequence modeling problem.<n>We propose STRIDE, which employs a lightweight masked diffusion module at the activation interface to jointly predict and progressively refine activation signals.
arXiv Detail & Related papers (2026-03-29T09:23:45Z) - VIPER: Process-aware Evaluation for Generative Video Reasoning [64.86465792516658]
We introduce VIPER, a comprehensive benchmark spanning 16 tasks across temporal, structural, symbolic, spatial, physics, and planning reasoning.<n>Our experiments reveal that state-of-the-art video models achieve only about 20% POC@1.0 and exhibit a significant outcome-hacking.
arXiv Detail & Related papers (2025-12-31T16:31:59Z) - StreamEQA: Towards Streaming Video Understanding for Embodied Scenarios [33.70462645363648]
StreamEQA is the first benchmark for streaming video question answering in embodied scenarios.<n>It is built upon 156 independent long videos and generates approximately 21K question-answer pairs with precise timestamps.<n>We hope StreamEQA will catalyze research on streaming video understanding for embodied applications.
arXiv Detail & Related papers (2025-12-04T04:48:16Z) - SVBench: A Benchmark with Temporal Multi-Turn Dialogues for Streaming Video Understanding [56.78088668917983]
We introduce SVBench, a pioneering benchmark with temporal multi-turn question-answering chains.<n>We design a semi-automated annotation pipeline to obtain 49,979 Question-Answer (QA) pairs of 1,353 streaming videos.<n>Our experimental results, obtained from 14 models in dialogue and streaming evaluations, reveal that while the closed-source GPT-4o outperforms others, most open-source LVLMs struggle with long-context streaming video understanding.
arXiv Detail & Related papers (2025-02-15T14:29:44Z) - Flash-VStream: Memory-Based Real-Time Understanding for Long Video Streams [78.72965584414368]
We present Flash-VStream, a video-language model that simulates the memory mechanism of human.
Compared to existing models, Flash-VStream achieves significant reductions in latency inference and VRAM consumption.
We propose VStream-QA, a novel question answering benchmark specifically designed for online video streaming understanding.
arXiv Detail & Related papers (2024-06-12T11:07:55Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.