8 citations · 16 across the 15 of their papers we have counts for
10 papers · 1 filter
MMS-LLaMA: Efficient LLM-based Audio-Visual Speech Recognition with Minimal Multimodal Speech Tokens
Jeong Hun Yeo, Hyeongseop Rha, Se Jin Park +1
Audio-Visual Speech Recognition (AVSR) achieves robust speech recognition in noisy environments by combining auditory and visual information. However, recent Large Language Model (…
Empathetic Response in Audio-Visual Conversations Using Emotion Preference Optimization and MambaCompressor
Yeonju Kim, Se Jin Park, Yong Man Ro
Chatbot research is advancing with the growing importance of chatbots in fields that require human interactions, such as customer support and mental health care. Despite these adva…
AV-EmoDialog: Chat with Audio-Visual Users Leveraging Emotional Cues
Se Jin Park, Yeonju Kim, Hyeongseop Rha +2
In human communication, both verbal and non-verbal cues play a crucial role in conveying emotions, intentions, and meaning beyond words alone. These non-linguistic information, suc…
Let's Go Real Talk: Spoken Dialogue Model for Face-to-Face Conversation
Se Jin Park, Chae Won Kim, Hyeongseop Rha +4
In this paper, we introduce a novel Face-to-Face spoken dialogue model. It processes audio-visual speech from user input and generates audio-visual speech as the response, marking…
AV2AV: Direct Audio-Visual Speech to Audio-Visual Speech Translation with Unified Audio-Visual Speech Representation
Jeongsoo Choi, Se Jin Park, Minsu Kim +1
This paper proposes a novel direct Audio-Visual Speech to Audio-Visual Speech Translation (AV2AV) framework, where the input and output of the system are multimodal (i.e., audio an…
DF-3DFace: One-to-Many Speech Synchronized 3D Face Animation with Diffusion
Se Jin Park, Joanna Hong, Minsu Kim +1
Speech-driven 3D facial animation has gained significant attention for its ability to create realistic and expressive facial animations in 3D space based on speech. Learning-based…
Exploring Phonetic Context-Aware Lip-Sync For Talking Face Generation
Se Jin Park, Minsu Kim, Jeongsoo Choi +1
Talking face generation is the challenging task of synthesizing a natural and realistic face that requires accurate synchronization with a given audio. Due to co-articulation, wher…
SyncTalkFace: Talking Face Generation with Precise Lip-Syncing via Audio-Lip Memory
Se Jin Park, Minsu Kim, Joanna Hong +2
The challenge of talking face generation from speech lies in aligning two different modal information, audio and video, such that the mouth region corresponds to input audio. Previ…
Test-time Adaptation for Real Image Denoising via Meta-transfer Learning
Agus Gunawan, Muhammad Adi Nugroho, Se Jin Park
In recent years, a ton of research has been conducted on real image denoising tasks. However, the efforts are more focused on improving real image denoising through creating a bett…
Multi-modality Associative Bridging through Memory: Speech Sound Recollected from Face Video
Minsu Kim, Joanna Hong, Se Jin Park +1
In this paper, we introduce a novel audio-visual multi-modal bridging framework that can utilize both audio and visual information, even with uni-modal inputs. We exploit a memory…