PhyAVBench: A Challenging Audio Physics-Sensitivity Benchmark for Physically Grounded Text-to-Audio-Video Generation

• URL: http://arxiv.org/abs/2512.23994v1
• Date: Tue, 30 Dec 2025 05:22:31 GMT
• Title: PhyAVBench: A Challenging Audio Physics-Sensitivity Benchmark for Physically Grounded Text-to-Audio-Video Generation
• Authors: Tianxin Xie, Wentao Lei, Guanjie Huang, Pengfei Zhang, Kai Jiang, Chunhui Zhang, Fengji Ma, Haoyu He, Han Zhang, Jiangshan He, Jinting Wang, Linghan Fang, Lufei Gao, Orkesh Ablet, Peihua Zhang, Ruolin Hu, Shengyu Li, Weilin Lin, Xiaoyang Feng, Xinyue Yang, Yan Rong, Yanyun Wang, Zihang Shao, Zelin Zhao, Chenxing Li, Shan Yang, Wenfu Wang, Meng Yu, Dong Yu, Li Liu,
• Abstract summary: Text-to-audio-video (T2AV) generation underpins a wide range of applications demanding realistic audio-visual content.<n>We present PhyAVBench, a challenging audio physics-sensitivity benchmark designed to evaluate the audio physics grounding capabilities of existing T2AV models.<n>Unlike prior benchmarks that primarily focus on audio-video synchronization, PhyAVBench explicitly evaluates models' understanding of the physical mechanisms underlying sound generation.
• Score: 63.3417467957431
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Text-to-audio-video (T2AV) generation underpins a wide range of applications demanding realistic audio-visual content, including virtual reality, world modeling, gaming, and filmmaking. However, existing T2AV models remain incapable of generating physically plausible sounds, primarily due to their limited understanding of physical principles. To situate current research progress, we present PhyAVBench, a challenging audio physics-sensitivity benchmark designed to systematically evaluate the audio physics grounding capabilities of existing T2AV models. PhyAVBench comprises 1,000 groups of paired text prompts with controlled physical variables that implicitly induce sound variations, enabling a fine-grained assessment of models' sensitivity to changes in underlying acoustic conditions. We term this evaluation paradigm the Audio-Physics Sensitivity Test (APST). Unlike prior benchmarks that primarily focus on audio-video synchronization, PhyAVBench explicitly evaluates models' understanding of the physical mechanisms underlying sound generation, covering 6 major audio physics dimensions, 4 daily scenarios (music, sound effects, speech, and their mix), and 50 fine-grained test points, ranging from fundamental aspects such as sound diffraction to more complex phenomena, e.g., Helmholtz resonance. Each test point consists of multiple groups of paired prompts, where each prompt is grounded by at least 20 newly recorded or collected real-world videos, thereby minimizing the risk of data leakage during model pre-training. Both prompts and videos are iteratively refined through rigorous human-involved error correction and quality control to ensure high quality. We argue that only models with a genuine grasp of audio-related physical principles can generate physically consistent audio-visual content. We hope PhyAVBench will stimulate future progress in this critical yet largely unexplored domain.

Related papers

• VABench: A Comprehensive Benchmark for Audio-Video Generation [22.00633729850902]
  VABench is a benchmark framework designed to evaluate the capabilities of synchronous audio-video generation.<n>It covers three task types: text-to-audio-video (T2AV), image-to-audio-video (I2AV), and stereo audio-video generation.<n>VABench covers seven major content categories: animals, human sounds, music, environmental sounds, synchronous physical sounds, complex scenes, and virtual worlds.
  arXiv  Detail & Related papers  (2025-12-10T03:57:29Z)
• PAVAS: Physics-Aware Video-to-Audio Synthesis [58.746986798623084]
  We present Physics-Aware Video-to-Audio Synthesis (PAVAS), a method that incorporates physical reasoning into a latent diffusion-based V2A generation.<n>We show that PAVAS produces physically plausible and perceptually coherent audio, outperforming existing V2A models in both quantitative and qualitative evaluations.
  arXiv  Detail & Related papers  (2025-12-09T06:28:50Z)
• PhysCorr: Dual-Reward DPO for Physics-Constrained Text-to-Video Generation with Automated Preference Selection [10.498184571108995]
  We propose PhysCorr, a unified framework for modeling, evaluating, and optimizing physical consistency in video generation.<n>Specifically, we introduce PhysicsRM, the first dual-dimensional reward model that quantifies both intra-object stability and inter-object interactions.<n>Our approach is model-agnostic and scalable, enabling seamless integration into a wide range of video diffusion and transformer-based backbones.
  arXiv  Detail & Related papers  (2025-11-06T02:40:57Z)
• Multi-Domain Audio Question Answering Toward Acoustic Content Reasoning in The DCASE 2025 Challenge [102.84031769492708]
  This task defines three QA subsets to test audio-language models on interactive question-answering over diverse acoustic scenes.<n>Preliminary results on the development set are compared, showing strong variation across models and subsets.<n>This challenge aims to advance the audio understanding and reasoning capabilities of audio-language models toward human-level acuity.
  arXiv  Detail & Related papers  (2025-05-12T09:04:16Z)
• Where are we in audio deepfake detection? A systematic analysis over generative and detection models [59.09338266364506]
  SONAR is a synthetic AI-Audio Detection Framework and Benchmark.<n>It provides a comprehensive evaluation for distinguishing cutting-edge AI-synthesized auditory content.<n>It is the first framework to uniformly benchmark AI-audio detection across both traditional and foundation model-based detection systems.
  arXiv  Detail & Related papers  (2024-10-06T01:03:42Z)
• AIR-Bench: Benchmarking Large Audio-Language Models via Generative Comprehension [95.8442896569132]
  We introduce AIR-Bench, the first benchmark to evaluate the ability of Large Audio-Language Models (LALMs) to understand various types of audio signals and interact with humans in the textual format. Results demonstrate a high level of consistency between GPT-4-based evaluation and human evaluation.
  arXiv  Detail & Related papers  (2024-02-12T15:41:22Z)
• Physics-Driven Diffusion Models for Impact Sound Synthesis from Videos [78.49864987061689]
  Traditional methods of impact sound synthesis use physics simulation to obtain a set of physics parameters that could represent and synthesize the sound. Existing video-driven deep learning-based approaches could only capture the weak correspondence between visual content and impact sounds. We propose a physics-driven diffusion model that can synthesize high-fidelity impact sound for a silent video clip.
  arXiv  Detail & Related papers  (2023-03-29T17:59:53Z)

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.