← Back to all news
arXiv AI By Kaidi Wang, Yi He, Wenhao Guan, Weijie Wu, Peijie Chen, Hongwu Ding, Xiong Zhang, Di Wu, Meng Meng, Jian Luan, Lin Li, Qingyang Hong

SyncVoice: Simple and Effective Automatic Video Dubbing with Vision-Augmented TTS

Read the original on arXiv AI →

SyncVoice is a new automatic video dubbing framework that adds a lightweight Text‑Visual Fusion Module to a pretrained TTS system, aligning visual features with linguistic representations to produce temporally synchronized speech. The approach avoids complex architectural changes and achieves state‑of‑the‑art performance on the LRS3 dataset in zero‑shot dubbing. When further trained on a large bilingual audio‑visual corpus, SyncVoice improves vocal fidelity while maintaining synchronization, enabling a single model to dub both Chinese and English videos.

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 AI.

Related stories

arXiv Computer Vision
Oct 2

Watch Your Speech: Text-aware Video-to-Speech Synthesis with Textual Conditioning

Watch Your Speech (WYS) is a video‑to‑speech synthesis framework that uses textual conditioning to resolve the one‑to‑many mapping problem inherent in silent talking‑face videos. It fuses textual context with video sequences via an attention‑based embedding module and employs a conditional flow matching objective to produce high‑fidelity, phonemically accurate speech. Experiments on LRS2 and LRS3 show that WYS sets new state‑of‑the‑art results in audio‑visual synchronization while maintaining competitive word‑error rates, and subjective tests confirm near‑human naturalness.

By Gunwoo Lee, Yoori Oh, Yoseob Han
ragdiffusionmultimodalbenchmarks
More like this →
arXiv Computer Vision
Oct 12

Mechanisms of Multimodal Synchronization: Insights from Decoder-Based Video-Text-to-Speech Synthesis

The paper investigates how sequence design influences temporal synchronization in unified decoder-only transformers for video‑text‑to‑speech synthesis. By training a Visatronic model on VoxCeleb2, the authors compare positional encoding schemes and modality ordering, finding that global sequential indexing and co‑temporal ordered indexing yield similar in‑domain performance, while text‑first co‑temporal indexing best transfers under domain shift. They also introduce TimeSync, a phoneme‑level metric that uncovers misalignments missed by frame‑level measures, and demonstrate that text and video provide complementary cues for intelligibility and temporal/emotional expressiveness.

By Akshita Gupta, Tatiana Likhomanenko, Karren Dai Yang, Richard He Bai, Zakaria Aldeneh, Navdeep Jaitly
llmsmultimodalsafety
More like this →
arXiv Computer Vision
Sep 30

ControlFoley: Unified and Controllable Video-to-Audio Generation with Cross-Modal Conflict Handling

arXiv:2604.15086v3 Announce Type: replace-cross Abstract: Recent advances in video-to-audio (V2A) generation enable high-quality audio synthesis from visual content, yet achieving robust and fine-gra...

By Jianxuan Yang, Xinyue Guo, Zhi Cheng, Kai Wang, Lipan Zhang, Jinjie Hu, Qiang Ji, Yihua Cao, Yihao Meng, Zhaoyue Cui, Mengmei Liu, Meng Meng, Jian Luan
multimodalbenchmarkssafety
More like this →