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DiFlowDubber: Discrete Flow Matching for Automated Video Dubbing via Cross-Modal Alignment and Synchronization
Ngoc-Son Nguyen, Thanh V. T. Tran, Jeongsoo Choi +3
Video dubbing requires content accuracy, expressive prosody, high-quality acoustics, and precise lip synchronization, yet existing approaches struggle on all four fronts. To addres…
VoiceCraft-Dub: Automated Video Dubbing with Neural Codec Language Models
Kim Sung-Bin, Jeongsoo Choi, Puyuan Peng +3
We present VoiceCraft-Dub, a novel approach for automated video dubbing that synthesizes high-quality speech from text and facial cues. This task has broad applications in filmmaki…
AlignDiT: Multimodal Aligned Diffusion Transformer for Synchronized Speech Generation
Jeongsoo Choi, Ji-Hoon Kim, Kim Sung-Bin +2
In this paper, we address the task of multimodal-to-speech generation, which aims to synthesize high-quality speech from multiple input modalities: text, video, and reference audio…
Deep Understanding of Sign Language for Sign to Subtitle Alignment
Youngjoon Jang, Jeongsoo Choi, Junseok Ahn +1
The objective of this work is to align asynchronous subtitles in sign language videos with limited labelled data. To achieve this goal, we propose a novel framework with the follow…
V2SFlow: Video-to-Speech Generation with Speech Decomposition and Rectified Flow
Jeongsoo Choi, Ji-Hoon Kim, Jinyu Li +2
In this paper, we introduce V2SFlow, a novel Video-to-Speech (V2S) framework designed to generate natural and intelligible speech directly from silent talking face videos. While re…
ARLON: Boosting Diffusion Transformers with Autoregressive Models for Long Video Generation
Zongyi Li, Shujie Hu, Shujie Liu +7
Text-to-video models have recently undergone rapid and substantial advancements. Nevertheless, due to limitations in data and computational resources, achieving efficient generatio…