arXiv Computation and Language By 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

Read the original on arXiv Computation and Language →

ViSSFlow is a unified flow‑matching framework that jointly tackles Video‑to‑Sound (V2S) and Visual Text‑to‑Speech (VisualTTS) generation. It incorporates a Diffusion Transformer with a disentangled condition aggregation mechanism, using cross‑attention for semantic cues and self‑attention for dense visual information. The authors also introduce a feature‑level data synthesis strategy to compensate for limited joint training data, and demonstrate that a single model can be trained end‑to‑end without sacrificing performance on either task.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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