HoliDubber introduces a patch-based autoregressive diffusion transformer for joint text-guided synthesis of speech and ambient audio in video dubbing, with a new benchmark showing outperformance over prior speech-only methods.
Deepdubber- v1: Towards high quality and dialogue, narration, monologue adaptive movie dubbing via multi-modal chain-of-thoughts reasoning guidance.arXiv preprint arXiv:2503.23660
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Talker-T2AV achieves better lip-sync accuracy, video quality, and audio quality than dual-branch baselines by separating high-level shared autoregressive modeling from modality-specific low-level diffusion refinement in a joint audio-video generation framework.
CoSyncDiT is a cognitive-inspired diffusion transformer that achieves state-of-the-art lip synchronization and naturalness in movie dubbing by guiding noise-to-speech generation through acoustic, visual, and contextual stages plus joint regularization.
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CoSyncDiT: Cognitive Synchronous Diffusion Transformer for Movie Dubbing
CoSyncDiT is a cognitive-inspired diffusion transformer that achieves state-of-the-art lip synchronization and naturalness in movie dubbing by guiding noise-to-speech generation through acoustic, visual, and contextual stages plus joint regularization.