This page organizes papers related to artificial intelligence published around the world. This page is summarized using Google Gemini and is operated on a non-profit basis. The copyright of the paper belongs to the author and the relevant institution. When sharing, simply cite the source.
VSSFlow: Unified Video-to-Sound and Visual Text-to-Speech Generation with Flow Matching
Outline
This paper presents VSSFlow, which integrates Video-to-Sound (V2S) and Visual Text-to-Speech (VisualTTS) tasks into a single framework. VSSFlow leverages a novel condition aggregation mechanism to handle different condition types and utilizes cross-attention and self-attention layers to leverage inductive biases tailored to the characteristics of each condition. Furthermore, it trains both tasks end-to-end together, achieving significant performance improvements without the need for complex training strategies.
Takeaways, Limitations
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Proposing a new flow-matching framework that integrates V2S and VisualTTS.
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Effectively handle different condition types by leveraging cross-attention and self-attention.
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Improving performance and ensuring training stability through end-to-end joint learning.
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Achieving state-of-the-art performance in V2S and VisualTTS benchmarks.
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Limitations is not specifically mentioned in the paper. (This part does not appear in the abstract, so it cannot be inferred.)
