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250809s2025 xx |||||o 00| ||eng c |
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|a 10.1109/TPAMI.2025.3597267
|2 doi
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|a pubmed25n1589.xml
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|a DE-627
|b ger
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|e rakwb
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|a eng
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| 100 |
1 |
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|a Li, Liang
|e verfasserin
|4 aut
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| 245 |
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|a Dubbing Movies via Hierarchical Phoneme Modeling and Acoustic Diffusion Denoising
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| 264 |
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|c 2025
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|2 rdacarrier
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|a Date Completed 03.10.2025
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|a Date Revised 03.10.2025
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|a published: Print
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|a Citation Status MEDLINE
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|a Given a piece of text, a video clip, and reference audio, the movie dubbing (also known as Visual Voice Cloning, V2C) task aims to generate speeches that clone reference voice and align well with the video in both emotion and lip movement, which is more challenging than conventional text-to-speech synthesis tasks. To align the generated speech with the inherent lip motion of the given silent video, most existing works utilize each video frame to query textual phonemes. However, such an attention operation usually leads to mumble speech because different phonemes are fused for video frames corresponding to one phoneme (video frames are finer-grained than phonemes). To address this issue, we propose a diffusion-based movie dubbing architecture, which improves pronunciation by Hierarchical Phoneme Modeling (HPM) and generates better mel-spectrogram through Acoustic Diffusion Denoising (ADD). We term our model as HD-Dubber. Specifically, our HPM bridges the visual information and corresponding speech prosody from three aspects: (1) aligning lip movement with the speech duration based on each phoneme unit by contrastive learning; (2) conveying facial expression to phoneme-level energy and pitch; and (3) injecting global emotions captured from video scenes into prosody. On the other hand, ADD exploits a denoising diffusion framework to transform the noise signal into a mel-spectrogram via a parameterized Markov chain conditioned on textual phonemes and reference audio. ADD has two novel denoisers, the Style-adaptive Residual Denoiser (SRD) and the Phoneme-enhanced U-net Denoiser (PUD), to enhance speaker similarity and improve pronunciation quality. Extensive experimental results on the three benchmark datasets demonstrate the state-of-the-art performance of the proposed method. The source code and trained models will be made available to the public
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|a Journal Article
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| 700 |
1 |
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|a Cong, Gaoxiang
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Qi, Yuankai
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zha, Zheng-Jun
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wu, Qi
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Sheng, Quan Z
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Huang, Qingming
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Yang, Ming-Hsuan
|e verfasserin
|4 aut
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| 773 |
0 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 47(2025), 11 vom: 02. Okt., Seite 10361-10377
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnas
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| 773 |
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|g volume:47
|g year:2025
|g number:11
|g day:02
|g month:10
|g pages:10361-10377
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| 856 |
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|u http://dx.doi.org/10.1109/TPAMI.2025.3597267
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