1 citations · 2 across the 27 of their papers we have counts for
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TAVID: Text-Driven Audio-Visual Interactive Dialogue Generation
Ji-Hoon Kim, Junseok Ahn, Doyeop Kwak +2
The objective of this paper is to jointly synthesize interactive videos and conversational speech from text and reference images. With the ultimate goal of building human-like conv…
Lost in Translation, Found in Embeddings: Sign Language Translation and Alignment
Youngjoon Jang, Liliane Momeni, Zifan Jiang +3
Our aim is to develop a unified model for sign language understanding, that performs sign language translation (SLT) and sign-subtitle alignment (SSA). Together, these two tasks en…
Test-Time Augmentation for Pose-invariant Face Recognition
Jaemin Jung, Youngjoon Jang, Joon Son Chung
The goal of this paper is to enhance face recognition performance by augmenting head poses during the testing phase. Existing methods often rely on training on frontalised images o…
Fork-Merge Decoding: Enhancing Multimodal Understanding in Audio-Visual Large Language Models
Chaeyoung Jung, Youngjoon Jang, Jongmin Choi +1
The goal of this work is to enhance balanced multimodal understanding in audio-visual large language models (AV-LLMs) by addressing modality bias without additional training. In cu…
AVCD: Mitigating Hallucinations in Audio-Visual Large Language Models through Contrastive Decoding
Chaeyoung Jung, Youngjoon Jang, Joon Son Chung
Hallucination remains a major challenge in multimodal large language models (MLLMs). To address this, various contrastive decoding (CD) methods have been proposed that contrasts or…
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…
Let Me Finish My Sentence: Video Temporal Grounding with Holistic Text Understanding
Jongbhin Woo, Hyeonggon Ryu, Youngjoon Jang +2
Video Temporal Grounding (VTG) aims to identify visual frames in a video clip that match text queries. Recent studies in VTG employ cross-attention to correlate visual frames and t…
Faces that Speak: Jointly Synthesising Talking Face and Speech from Text
Youngjoon Jang, Ji-Hoon Kim, Junseok Ahn +6
The goal of this work is to simultaneously generate natural talking faces and speech outputs from text. We achieve this by integrating Talking Face Generation (TFG) and Text-to-Spe…