时光回响：解锁视频到音频生成模型的长度泛化能力

摘要

视频与音频的多模态对齐任务面临规模化挑战，这主要源于数据稀缺以及文本描述与帧级视频信息之间的不匹配。本研究针对多模态到音频生成中的规模化难题，探究在短样本上训练的模型能否在测试时泛化至长样本。为此，我们提出名为MMHNet的多模态分层网络——一种对现有视频到音频生成模型的增强扩展。该方案通过融合分层方法与非因果Mamba架构，实现了长序列音频的生成能力。我们提出的方法显著提升了长音频生成性能，可支持超过5分钟的连续生成。实验证明，在未进行长样本训练的情况下，视频到音频生成任务中"短训长测"具有可行性。在长视频到音频的基准测试中，本方法取得了显著优于现有视频到音频技术的效果。特别值得指出的是，当先前方法在长时生成任务中表现受限时，我们的模型成功实现了超5分钟的连续生成。

English

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.

时光回响：解锁视频到音频生成模型的长度泛化能力

Echoes Over Time: Unlocking Length Generalization in Video-to-Audio Generation Models

摘要

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