The paper introduces Align Then Reason (ATR), a multilingual lip‑sync judge that aligns frame‑level lip representations with phonetic units of a candidate text line and then uses a language model to evaluate both content and timing. ATR achieves significant improvements over existing baselines on a seven‑language benchmark, with mean AUC gains of up to 59.4% for 2B reasoners and similar gains across other LLM families. The method also transfers well to unseen languages and outperforms lip‑reading baselines on real dubbing tasks such as dub‑line reranking and script‑to‑clip assignment.
By Rui Liu, Bhavin Jawade, Haoqi Li, Shivam Mehta, Karan Saxena, Yinghong Lan, Cameron R. Wolfe
arXiv:2606. 01031v1 Announce Type: cross Abstract: Audio-driven talking-head generation has advanced rapidly, yet existing evaluation protocols mainly rely on frame-wise metrics that assume strict temporal correspondence between generated and reference videos.
By Zhicheng Zhang, Lei Wang, Yu Zhang, Yongsheng Gao
arXiv:2610.08417v1 Announce Type: new
Abstract: Recent advances in LipSync generation technology have led to the creation of highly realistic videos, posing severe societal risks. However, existing d...
By Tianyi She, Jiawei Liu, Weifeng Liu, Hanqing Zhao, Weiming Zhang, Kejiang Chen
Recent advances in LipSync generation technology have led to the creation of highly realistic videos, posing severe societal risks. However, existing defense strategies struggle against LipSync forger...
arXiv:2609.38019v1 Announce Type: new
Abstract: We present RGOR (Reference-Grounded Oral Refinement), an audio-driven lip-sync framework that renders the mouth of the specific person being dubbed rat...
By Bangxun Tang
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
arXiv:2609.10366v1 Announce Type: cross
Abstract: While AVSR has achieved sub-1% word error rates on the standard LRS3 benchmark, its reliance on broadcast speech obscures whether this reflects true...
By Rishabh Jain, Naomi Harte
arXiv:2610.19290v1 Announce Type: new
Abstract: We present Kandinsky S2V 1.0, an open family of speech-to-video models for controllable audio-driven human animation, comprising Kandinsky S2V 1.0 Lite...
By Nikolay Semenov, Artem Riabinin, Sergey Bassenko, Anna Averchenkova, Julia Agafonova, Anastasia Aliaskina, Kirill Kozlov, Lev Novitskiy, Nikita Samsonov, Nikolai Gerasimenko, Denis Dimitrov
arXiv:2610.23558v1 Announce Type: new
Abstract: Audio-driven lip synchronization aims to match target speech through localized mouth and jaw articulation while preserving subject identity, head pose,...
By Huaishao Luo, Huan Zhang, Guanbo Bao, Yinglu Liu, Youzheng Wu, Xiaodong He
Vorch-Human is a unified framework for human‑centric audio‑visual generation that handles multiple tasks—animating a person from speech, jointly generating speech and video from a voice reference, and synthesizing a scene from paired appearance and voice references—using a single dual‑stream audio‑video diffusion transformer. The model incorporates clean condition‑audio and condition‑video tokens, per‑token task embeddings, temporal position types, condition masks, and a shared multimodal prompt encoder to express diverse inputs such as driving speech, timbre examples, first frames, and subject images. A two‑level data pipeline supplies the necessary supervision by extracting speech, appearance, and timbre annotations from clips and linking consistent identity and outfit references across videos, while a frozen‑prefix recurrence enables long‑form audio‑driven generation with reduced boundary discontinuity and identity drift.
By Yang Ding, Haoran Yu, Xin Ma, Yulei Lu, Menglin Han, Yaole Wang, Siqian Yang, Gang Yue, Kaihao Zhang, Yaohui Wang, Lin Ma
arXiv:2609.10394v1 Announce Type: cross
Abstract: Current audio-visual speech recognition (AVSR) benchmarks, like LRS3, rely heavily on clean, scripted and rehearsed speech. They fail to reflect the...
By Rishabh Jain, Aristeidis Papadopoulos, Zhaofeng Lin, Naomi Harte
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.
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