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AGAV-Rater: Adapting Large Multimodal Model for AI-Generated Audio-Visual Quality Assessment

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arxiv 2501.18314 v2 pith:55HQICF6 submitted 2025-01-30 cs.MM cs.CVcs.SDeess.AS

Yuqin Cao , Xiongkuo Min , Yixuan Gao , Wei Sun , Guangtao Zhai This is my paper · ORCID
classification cs.MMcs.CVcs.SDeess.AS
keywords qualityagav-rateragavassessmentaudio-visualmethodsagavqa-3kagavs
verification ladder T0 review T1 audit T2 compute T3 formal
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abstract

Many video-to-audio (VTA) methods have been proposed for dubbing silent AI-generated videos. An efficient quality assessment method for AI-generated audio-visual content (AGAV) is crucial for ensuring audio-visual quality. Existing audio-visual quality assessment methods struggle with unique distortions in AGAVs, such as unrealistic and inconsistent elements. To address this, we introduce AGAVQA-3k, the first large-scale AGAV quality assessment dataset, comprising $3,382$ AGAVs from $16$ VTA methods. AGAVQA-3k includes two subsets: AGAVQA-MOS, which provides multi-dimensional scores for audio quality, content consistency, and overall quality, and AGAVQA-Pair, designed for optimal AGAV pair selection. We further propose AGAV-Rater, a LMM-based model that can score AGAVs, as well as audio and music generated from text, across multiple dimensions, and selects the best AGAV generated by VTA methods to present to the user. AGAV-Rater achieves state-of-the-art performance on AGAVQA-3k, Text-to-Audio, and Text-to-Music datasets. Subjective tests also confirm that AGAV-Rater enhances VTA performance and user experience. The dataset and code is available at https://github.com/charlotte9524/AGAV-Rater.

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Cited by 2 Pith papers

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  1. Efficient Face Image Quality Assessment via Self-training and Knowledge Distillation

    cs.CV 2025-07 conditional novelty 5.0 of 10

    A self-trained teacher and knowledge distillation produce a 1.18M-parameter face image quality model that nearly matches the 86.88M-parameter teacher on ICCV 2025 VQualA FIQA challenge data.

  2. Engagement Prediction of Short Videos with Large Multimodal Models

    cs.CV 2025-08 conditional novelty 4.0 of 10

    Large multimodal models, especially one that also processes audio, predict short-video engagement better than the prior feature-based baseline on the SnapUGC test set.

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