PRISM-Bench: An Audio-Centric Diagnostic Benchmark for Text-to-Audio-Video Generation
- URL: http://arxiv.org/abs/2609.04867v2
- Date: Wed, 09 Sep 2026 03:05:45 GMT
- Title: PRISM-Bench: An Audio-Centric Diagnostic Benchmark for Text-to-Audio-Video Generation
- Abstract summary: Text-to-audio-video (T2AV) generation has advanced rapidly, but its evaluation still underestimates the audio modality.<n>We present PRISM-Bench, the first audio-centric diagnostic benchmark for T2AV generation.
- Score: 23.127789678422698
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Text-to-audio-video (T2AV) generation has advanced rapidly, but its evaluation still underestimates the audio modality. Existing benchmarks either treat audio as an auxiliary component of video quality or assess it in isolation from audiovisual grounding, making it difficult to diagnose where current systems truly succeed or fail in audio generation. We present PRISM-Bench, the first audio-centric diagnostic benchmark for T2AV generation. Built from a rigorously curated dataset of 900 human-verified samples, PRISM-Bench factorizes audio evaluation along two orthogonal axes: audio type (Speech, Music, and Sound) and sound-source visibility (On-screen vs. Off-screen). It evaluates generated content across four perceptual dimensions (Audio-Visual Coherence, Audio Quality, Audio Expressiveness, and Prompt Following) with 35 fine-grained criteria. To ensure reliable assessment, we adopt an enhanced MLLM-as-a-Judge protocol based on blind, side-by-side comparison against ground-truth references, demonstrating strong alignment (over 70% mean agreement) with human raters. Our evaluation of recent T2AV systems highlights a significant performance gap between frontier and open-source models. Furthermore, we demonstrate that current generation paradigms overfit to perceptual fidelity while struggling with complex grounding and control tasks, particularly in generating music and synchronized On-screen audio.
Related papers
- VidAudio-Bench: Benchmarking V2A and VT2A Generation across Four Audio Categories [47.95995227527911]
VidAudio-Bench is a multi-task benchmark for V2A evaluation with four key features.<n>It comprises 1,634 video-text pairs and benchmarks 11 state-of-the-art generation models.<n>It validates all metrics through subjective studies, demonstrating strong consistency with human preferences.
arXiv Detail & Related papers (2026-04-12T09:11:55Z) - 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) - Omni2Sound: Towards Unified Video-Text-to-Audio Generation [56.11583645408007]
Training a unified model integrating video-to-audio (V2A), text-to-audio (T2A) and joint video-text-to-audio (VT2A) generation offers significant application flexibility.<n>SoundAtlas is a large-scale dataset (470k pairs) that significantly outperforms existing benchmarks and even human experts in quality.<n>We propose Omni2Sound, a unified VT2A diffusion model supporting flexible input modalities.
arXiv Detail & Related papers (2026-01-06T05:49:41Z) - T2AV-Compass: Towards Unified Evaluation for Text-to-Audio-Video Generation [41.03487954415606]
Text-to-Audio-Video (T2AV) generation aims to synthesize temporally coherent video and semantically synchronized audio from natural language.<n>We present T2AV-, a unified benchmark for comprehensive evaluation of T2AV systems.<n>Even the strongest models fall substantially short of human-level realism and cross-modal consistency.
arXiv Detail & Related papers (2025-12-24T10:30:35Z) - 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) - ViSAudio: End-to-End Video-Driven Binaural Spatial Audio Generation [55.76423101183408]
ViSAudio is an end-to-end framework that employs conditional flow matching with a dual-branch audio generation architecture.<n>It generates high-quality audio with spatial immersion that adapts to viewpoint changes, sound-source motion, and diverse acoustic environments.
arXiv Detail & Related papers (2025-12-02T18:56:12Z) - Frieren: Efficient Video-to-Audio Generation Network with Rectified Flow Matching [51.70360630470263]
Video-to-audio (V2A) generation aims to synthesize content-matching audio from silent video.<n>We propose Frieren, a V2A model based on rectified flow matching.<n>Experiments indicate that Frieren achieves state-of-the-art performance in both generation quality and temporal alignment.
arXiv Detail & Related papers (2024-06-01T06:40:22Z) - AdVerb: Visually Guided Audio Dereverberation [49.958724234969445]
We present AdVerb, a novel audio-visual dereverberation framework.
It uses visual cues in addition to the reverberant sound to estimate clean audio.
arXiv Detail & Related papers (2023-08-23T18:20:59Z) - Make-An-Audio: Text-To-Audio Generation with Prompt-Enhanced Diffusion
Models [65.18102159618631]
multimodal generative modeling has created milestones in text-to-image and text-to-video generation.
Its application to audio still lags behind for two main reasons: the lack of large-scale datasets with high-quality text-audio pairs, and the complexity of modeling long continuous audio data.
We propose Make-An-Audio with a prompt-enhanced diffusion model that addresses these gaps.
arXiv Detail & Related papers (2023-01-30T04:44:34Z) - NAViDAd: A No-Reference Audio-Visual Quality Metric Based on a Deep
Autoencoder [0.0]
We propose a No-Reference Audio-Visual Quality Metric Based on a Deep Autoencoder (NAViDAd)
The model is formed by a 2-layer framework that includes a deep autoencoder layer and a classification layer.
The model performed well when tested against the UnB-AV and the LiveNetflix-II databases.
arXiv Detail & Related papers (2020-01-30T15:40:08Z)
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.