English(EN) ViSSFlow: Unifying Video-Conditioned Sound and Speech Generation via Joint Learning

ViSSFlow 统一视频条件声音和语音生成

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-04 05:00

研究人员开发了 ViSSFlow，一个统一视频条件声音和语音生成的新框架。该方法利用具有解耦条件聚合机制的 Diffusion Transformer (DiT) 来处理各种视觉和语义线索。ViSSFlow 表明，单个模型可以在不降低性能的情况下有效处理视频到声音 (V2S) 和视觉文本到语音 (VisualTTS) 任务，并通过特征级数据合成策略解决了配对训练数据稀缺的问题。 AI

影响 ViSSFlow 提供了一种统一的视频条件音频生成方法，有可能简化涉及视觉上下文的任务的开发并提高其性能。

排序理由该集群包含一篇详细介绍音频生成新模型架构和方法的 ist 研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

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报道来源 [1]

arXiv cs.CL TIER_1 English(EN) · Xin Cheng, Yuyue Wang, Xihua Wang, Yihan Wu, Kaisi Guan, Yijing Chen, Peng Zhang, Xiaojiang Liu, Meng Cao, Ruihua Song · 2026-11-04 05:00

ViSSFlow：通过联合学习统一视频条件声音和语音生成

arXiv:2509.24773v5 Announce Type: replace-cross Abstract: Video-conditioned audio generation, including Video-to-Sound (V2S) and Visual Text-to-Speech (VisualTTS), has traditionally been studied as two separate tasks despite their shared objective of generating audio conditioned …

本文如何被排名

报道来源 [1]

ViSSFlow：通过联合学习统一视频条件声音和语音生成

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