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ViSSFlow unifies video-conditioned sound and speech generation

By PulseAugur Editorial · [1 sources] ·

Researchers have developed ViSSFlow, a novel framework that unifies video-conditioned sound and speech generation. This approach utilizes a Diffusion Transformer (DiT) with a disentangled condition aggregation mechanism to process diverse visual and semantic cues. ViSSFlow demonstrates that a single model can effectively handle both Video-to-Sound (V2S) and Visual Text-to-Speech (VisualTTS) tasks without performance degradation, addressing the scarcity of paired training data through a feature-level data synthesis strategy. AI

IMPACT ViSSFlow offers a unified approach to video-conditioned audio generation, potentially streamlining development and improving performance for tasks involving visual context.

RANK_REASON The cluster contains a research paper detailing a new model architecture and methodology for audio generation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

ViSSFlow unifies video-conditioned sound and speech generation

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38 / 100
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The cluster contains a research paper detailing a new model architecture and methodology for audio generation. [lever_c_demoted from research: ic=1 ai=1.0]
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High
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Breaking (< 6h)
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COVERAGE [1]

  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 ·

    ViSSFlow: Unifying Video-Conditioned Sound and Speech Generation via Joint Learning

    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 …

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