PhyAVBench: A Challenging Audio Physics-Sensitivity Benchmark for Physically Grounded Text-to-Audio-Video Generation
On the map: World models → Video-generation world models and simulators
Abstract (arXiv)
Text-to-audio-video (T2AV) generation is central to applications such as filmmaking and world modeling. However, current models often fail to produce physically plausible sounds. Previous benchmarks primarily focus on audio-video temporal synchronization, while largely overlooking explicit evaluation of audio-physics grounding, thereby limiting the study of physically plausible audio-visual generation. To address this issue, we present PhyAVBench, the first benchmark that systematically evaluates the audio-physics grounding capabilities of T2AV, image-to-audio-video (I2AV), and video-to-audio (V2A) models. PhyAVBench offers PhyAV-Sound-11K, a new dataset of 25.5 hours of 11,605 audible videos collected from 184 participants to ensure diversity and avoid data leakage. It contains 337 paired-prompt groups with controlled physical variations that drive sound differences, each grounded with an average of 17 videos and spanning 6 audio-physics dimensions and 41 fine-grained test points. Each prompt pair is annotated with the physical factors underlying their acoustic differences. Importantly, PhyAVBench leverages paired text prompts to evaluate this capability. We term this evaluation paradigm the Audio-Physics Sensitivity Test (APST) and introduce a novel metric, the Contrastive Physical Response Score (CPRS), which quantifies the acoustic consistency between generated videos and their real-world counterparts. We conduct a comprehensive evaluation of 17 state-of-the-art models. Our results reveal that even leading commercial models struggle with fundamental audio-physical phenomena, exposing a critical gap beyond audio-visual synchronization and pointing to future research directions. We hope PhyAVBench will serve as a foundation for advancing physically grounded audio-visual generation. Prompts, ground-truth, and generated video samples are available at https://github.com/imxtx/PhyAVBench.
Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Placed in Video-generation world models and simulators, one of 11 directions in World models. Builds on 19 of its 52 references that are in the map. First posted on arXiv 30 Dec 2025.
Builds on
19 of 52 references.
References · 19
- 2022CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformerscited by 985
- 2023ModelScope Text-to-Video Technical Reportcited by 658
- 2021GODIVA: Generating Open-DomaIn Videos from nAtural Descriptionscited by 256
- 2024FoleyCrafter: Bring Silent Videos to Life with Lifelike and Synchronized Soundscited by 110
- 2025Ovi: Twin Backbone Cross-Modal Fusion for Audio-Video Generationcited by 83
- 2025Seedance 1.5 pro: A Native Audio-Visual Joint Generation Foundation Modelcited by 80
- 2025UniVerse-1: Unified Audio-Video Generation via Stitching of Expertscited by 61
- 2025HunyuanVideo-Foley: Multimodal Diffusion with Representation Alignment for High-Fidelity Foley Audio Generationcited by 46
- 2024AV-DiT: Efficient Audio-Visual Diffusion Transformer for Joint Audio and Video Generationcited by 41
- 2025ThinkSound: Chain-of-Thought Reasoning in Multimodal Large Language Models for Audio Generation and Editingcited by 34
- 2025VABench: A Comprehensive Benchmark for Audio-Video Generationcited by 30
- 2024Text-to-Audio Generation Synchronized with Videoscited by 28
7 more
- 2024SyncFlow: Toward Temporally Aligned Joint Audio-Video Generation from Textcited by 26
- 2025T2AV-Compass: Towards Unified Evaluation for Text-to-Audio-Video Generationcited by 24
- 2025UniForm: A Unified Multi-Task Diffusion Transformer for Audio-Video Generationcited by 22
- 2024PEAVS: Perceptual Evaluation of Audio-Visual Synchrony Grounded in Viewers' Opinion Scorescited by 13
- 2024SAVGBench: Benchmarking Spatially Aligned Audio-Video Generationcited by 10
- 2025Long-Video Audio Synthesis with Multi-Agent Collaborationcited by 8
- 2025FoleyBench: A Benchmark For Video-to-Audio Modelscited by 7
Built on by
Citing papers per year: 2026: 11.
11 papers.
- 2026MSAVBench: Towards Comprehensive and Reliable Evaluation of Multi-Shot Audio-Video Generationcited by 10
- 2026LongAV-Compass: Towards Unified Evaluation of Minute-Scale Audio-Visual Generation Across T2AV, I2AV, and V2AVcited by 9
- 2026Do Joint Audio-Video Generation Models Understand Physics?cited by 8
- 2026MTAVG-Bench 2.0: Diagnosing Failure Modes of Cinematic Expressiveness in Multi-Talker Audio-Video Generationcited by 4
- 2026OmniHuman: A Large-scale Dataset and Benchmark for Human-Centric Video Generationcited by 3
- 2026Audio-Visual Intelligence in Large Foundation Modelscited by 2
- 2026Beyond Coherence: Benchmarking Professional Editing-Technique Execution in Multi-Shot Audio-Video Generationcited by 1
- 2026VTR-Bench: A Systematic Benchmark for Evaluating Visual Text Rendering in Video Generationcited by 1
- 2026VeriPhy: Agentic Physical Reasoning for World Model Evaluation and Refinementcited by 0
- 2026PRISM-Bench: An Audio-Centric Diagnostic Benchmark for Text-to-Audio-Video Generationcited by 0
- 2026PIVOT: Physics-Grounded Verification for AI-Generated Audio-Video Detectioncited by 0
- 2026VTR-Bench: A Systematic Benchmark for Evaluating Visual Text Rendering in Video Generation1 Oct 2026
- 2026PIVOT: Physics-Grounded Verification for AI-Generated Audio-Video Detection14 Sep 2026
- 2026Beyond Coherence: Benchmarking Professional Editing-Technique Execution in Multi-Shot Audio-Video Generation8 Sep 2026
- 2026PRISM-Bench: An Audio-Centric Diagnostic Benchmark for Text-to-Audio-Video Generation4 Sep 2026
- 2026VeriPhy: Agentic Physical Reasoning for World Model Evaluation and Refinement2 Sep 2026
- 2026MTAVG-Bench 2.0: Diagnosing Failure Modes of Cinematic Expressiveness in Multi-Talker Audio-Video Generation27 May 2026
- 2026LongAV-Compass: Towards Unified Evaluation of Minute-Scale Audio-Visual Generation Across T2AV, I2AV, and V2AV25 May 2026
- 2026MSAVBench: Towards Comprehensive and Reliable Evaluation of Multi-Shot Audio-Video Generation19 May 2026
- 2026Do Joint Audio-Video Generation Models Understand Physics?8 May 2026
- 2026Audio-Visual Intelligence in Large Foundation Models5 May 2026
- 2026OmniHuman: A Large-scale Dataset and Benchmark for Human-Centric Video Generation20 Apr 2026
Similar, not cited
Close in wording, no citation either way
- 2025PAVAS: Physics-Aware Video-to-Audio Synthesis
- 2025Syncphony: Synchronized Audio-to-Video Generation with Diffusion Transformers
- 2026AVGen-Bench: A Task-Driven Benchmark for Multi-Granular Evaluation of Text-to-Audio-Video Generation
- 2024TAVGBench: Benchmarking Text to Audible-Video Generation
- 2026MOVA: Towards Scalable and Synchronized Video-Audio Generation
- 2026Soundwich: Video Generation with Layered and Controllable Audio
- 2026AVBench: Human-Aligned and Automated Evaluation Benchmark for Audio-Video Generative Models
- 2025"PhyWorldBench": A Comprehensive Evaluation of Physical Realism in Text-to-Video Models