# PhyAVBench: A Challenging Audio Physics-Sensitivity Benchmark for Physically Grounded Text-to-Audio-Video Generation arXiv: https://arxiv.org/abs/2512.23994 ยท HTML: https://mlsanity.com/paper/2512.23994/ ## 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. ## Placed in - World models / [Video-generation world models and simulators](https://mlsanity.com/wm/video-generation-world-models-and-simulators/) ## Builds on (most cited first, 19 of 19) - [CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers](https://mlsanity.com/paper/2205.15868/) (2022) - [ModelScope Text-to-Video Technical Report](https://mlsanity.com/paper/2308.06571/) (2023) - [GODIVA: Generating Open-DomaIn Videos from nAtural Descriptions](https://mlsanity.com/paper/2104.14806/) (2021) - [FoleyCrafter: Bring Silent Videos to Life with Lifelike and Synchronized Sounds](https://mlsanity.com/paper/2407.01494/) (2024) - [Ovi: Twin Backbone Cross-Modal Fusion for Audio-Video Generation](https://mlsanity.com/paper/2510.01284/) (2025) - [Seedance 1.5 pro: A Native Audio-Visual Joint Generation Foundation Model](https://mlsanity.com/paper/2512.13507/) (2025) - [UniVerse-1: Unified Audio-Video Generation via Stitching of Experts](https://mlsanity.com/paper/2509.06155/) (2025) - [HunyuanVideo-Foley: Multimodal Diffusion with Representation Alignment for High-Fidelity Foley Audio Generation](https://mlsanity.com/paper/2508.16930/) (2025) - [AV-DiT: Efficient Audio-Visual Diffusion Transformer for Joint Audio and Video Generation](https://mlsanity.com/paper/2406.07686/) (2024) - [ThinkSound: Chain-of-Thought Reasoning in Multimodal Large Language Models for Audio Generation and Editing](https://mlsanity.com/paper/2506.21448/) (2025) - [VABench: A Comprehensive Benchmark for Audio-Video Generation](https://mlsanity.com/paper/2512.09299/) (2025) - [Text-to-Audio Generation Synchronized with Videos](https://mlsanity.com/paper/2403.07938/) (2024) - [SyncFlow: Toward Temporally Aligned Joint Audio-Video Generation from Text](https://mlsanity.com/paper/2412.15220/) (2024) - [T2AV-Compass: Towards Unified Evaluation for Text-to-Audio-Video Generation](https://mlsanity.com/paper/2512.21094/) (2025) - [UniForm: A Unified Multi-Task Diffusion Transformer for Audio-Video Generation](https://mlsanity.com/paper/2502.03897/) (2025) - [PEAVS: Perceptual Evaluation of Audio-Visual Synchrony Grounded in Viewers' Opinion Scores](https://mlsanity.com/paper/2404.07336/) (2024) - [SAVGBench: Benchmarking Spatially Aligned Audio-Video Generation](https://mlsanity.com/paper/2412.13462/) (2024) - [Long-Video Audio Synthesis with Multi-Agent Collaboration](https://mlsanity.com/paper/2503.10719/) (2025) - [FoleyBench: A Benchmark For Video-to-Audio Models](https://mlsanity.com/paper/2511.13219/) (2025) ## Built on by (most cited first, 11 of 11) - [MSAVBench: Towards Comprehensive and Reliable Evaluation of Multi-Shot Audio-Video Generation](https://mlsanity.com/paper/2605.20183/) (2026) - [LongAV-Compass: Towards Unified Evaluation of Minute-Scale Audio-Visual Generation Across T2AV, I2AV, and V2AV](https://mlsanity.com/paper/2605.26244/) (2026) - [Do Joint Audio-Video Generation Models Understand Physics?](https://mlsanity.com/paper/2605.07061/) (2026) - [MTAVG-Bench 2.0: Diagnosing Failure Modes of Cinematic Expressiveness in Multi-Talker Audio-Video Generation](https://mlsanity.com/paper/2605.28035/) (2026) - [OmniHuman: A Large-scale Dataset and Benchmark for Human-Centric Video Generation](https://mlsanity.com/paper/2604.18326/) (2026) - [Audio-Visual Intelligence in Large Foundation Models](https://mlsanity.com/paper/2605.04045/) (2026) - [Beyond Coherence: Benchmarking Professional Editing-Technique Execution in Multi-Shot Audio-Video Generation](https://mlsanity.com/paper/2609.08275/) (2026) - [VTR-Bench: A Systematic Benchmark for Evaluating Visual Text Rendering in Video Generation](https://mlsanity.com/paper/2610.01499/) (2026) - [VeriPhy: Agentic Physical Reasoning for World Model Evaluation and Refinement](https://mlsanity.com/paper/2609.03153/) (2026) - [PRISM-Bench: An Audio-Centric Diagnostic Benchmark for Text-to-Audio-Video Generation](https://mlsanity.com/paper/2609.04867/) (2026) - [PIVOT: Physics-Grounded Verification for AI-Generated Audio-Video Detection](https://mlsanity.com/paper/2609.15562/) (2026) ## 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.