← 研踪 PaperTrailCVF · CVPR 2026:cvf:e7e41e61a133feeb30f6a61ebd6816dc · 记录 #1
Echoes Over Time: Unlocking Length Generalization in Video-to-Audio Generation Models
Simon, ChristianIshii, MasatoWang, Wei-YaoSaito, KoichiHayakawa, AkioShim, DongseokZhong, ZhiCui, ShuyangShibuya, TakashiTakahashi, ShusukeMitsufuji, Yuki 来源日期（仅到年份） 2026
来源记录日期 2026
雷达观测 2026-11-05 02:06
摘要
Scaling multimodal alignment between video and audio is challenging, particularly due to limited data and the mismatch between text descriptions and frame-level video information. In this work, we tackle the scaling challenge in multimodal-to-audio generation, examining whether models trained on short instances can generalize to longer ones during testing. To tackle this challenge, we present multimodal hierarchical networks so-called MMHNet, an enhanced extension of state-of-the-art video-to-audio models. Our approach integrates a hierarchical method and non-causal Mamba to support long-form audio generation. Our proposed method significantly improves long audio generation up to more than 5 minutes. We also prove that training short and testing long is possible in the video-to-audio generation tasks without training on the longer durations. We show in our experiments that our proposed method could achieve remarkable results on long-video to audio benchmarks, beating prior works in video-to-audio tasks. Moreover, we showcase our model capability in generating more than 5 minutes, while prior video-to-audio methods fall short in generating with long durations. Our project page: https://echoesovertime.github.io
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记录 #1 · 2026 · 最新观测版本
Echoes Over Time: Unlocking Length Generalization in Video-to-Audio Generation Models
Scaling multimodal alignment between video and audio is challenging, particularly due to limited data and the mismatch between text descriptions and frame-level video information. In this work, we tackle the scaling challenge in multimodal-to-audio generation, examining whether models trained on short instances can generalize to longer ones during testing. To tackle this challenge, we present multimodal hierarchical networks so-called MMHNet, an enhanced extension of state-of-the-art video-to-audio models. Our approach integrates a hierarchical method and non-causal Mamba to support long-form audio generation. Our proposed method significantly improves long audio generation up to more than 5 minutes. We also prove that training short and testing long is possible in the video-to-audio generation tasks without training on the longer durations. We show in our experiments that our proposed method could achieve remarkable results on long-video to audio benchmarks, beating prior works in video-to-audio tasks. Moreover, we showcase our model capability in generating more than 5 minutes, while prior video-to-audio methods fall short in generating with long durations. Our project page: https://echoesovertime.github.io
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