2 citations · 5 across the 10 of their papers we have counts for
12 papers
Physics-Aware Deepfake Detection via Distance-Speech Consistency
Kyeongrae Kim, Kim Sung-Bin, Oh Hyun-Bin +1
Recent progress in generative models has made talking-head deepfakes highly realistic and easy to create, jeopardizing public trust. However, most audio-visual deepfake detectors r…
SMILE-Next: Teaching Large Language Models to Detect, Classify, and Reason about Laughter
Lee Jung-Mok, Kim Sung-Bin, Joohyun Chang +2
Laughter is a complex social signal that conveys communicative intent beyond amusement. While prior work has focused on isolated laughter analysis tasks, a comprehensive understand…
A Language-Guided Bayesian Optimization for Efficient LoRA Hyperparameter Search
Baek Seong-Eun, Lee Jung-Mok, Kim Sung-Bin +1
Fine-tuning Large Language Models (LLMs) with Low-Rank Adaptation (LoRA) offers a resource-efficient way to personalize or specialize. However, LoRA is highly sensitive to hyperpar…
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…
Perceptually Accurate 3D Talking Head Generation: New Definitions, Speech-Mesh Representation, and Evaluation Metrics
Lee Chae-Yeon, Oh Hyun-Bin, Han EunGi +3
Recent advancements in speech-driven 3D talking head generation have made significant progress in lip synchronization. However, existing models still struggle to capture the percep…
SoundBrush: Sound as a Brush for Visual Scene Editing
Kim Sung-Bin, Kim Jun-Seong, Junseok Ko +2
We propose SoundBrush, a model that uses sound as a brush to edit and manipulate visual scenes. We extend the generative capabilities of the Latent Diffusion Model (LDM) to incorpo…
Sound2Vision: Generating Diverse Visuals from Audio through Cross-Modal Latent Alignment
Kim Sung-Bin, Arda Senocak, Hyunwoo Ha +1
How does audio describe the world around us? In this work, we propose a method for generating images of visual scenes from diverse in-the-wild sounds. This cross-modal generation t…
AVHBench: A Cross-Modal Hallucination Benchmark for Audio-Visual Large Language Models
Kim Sung-Bin, Oh Hyun-Bin, JungMok Lee +3
Following the success of Large Language Models (LLMs), expanding their boundaries to new modalities represents a significant paradigm shift in multimodal understanding. Human perce…
Enhancing Speech-Driven 3D Facial Animation with Audio-Visual Guidance from Lip Reading Expert
Han EunGi, Oh Hyun-Bin, Kim Sung-Bin +4
Speech-driven 3D facial animation has recently garnered attention due to its cost-effective usability in multimedia production. However, most current advances overlook the intellig…
Revisiting Learning-based Video Motion Magnification for Real-time Processing
Hyunwoo Ha, Oh Hyun-Bin, Kim Jun-Seong +6
Video motion magnification is a technique to capture and amplify subtle motion in a video that is invisible to the naked eye. The deep learning-based prior work successfully demons…
The Devil in the Details: Simple and Effective Optical Flow Synthetic Data Generation
Kwon Byung-Ki, Kim Sung-Bin, Tae-Hyun Oh
Recent work on dense optical flow has shown significant progress, primarily in a supervised learning manner requiring a large amount of labeled data. Due to the expensiveness of ob…
Sound to Visual Scene Generation by Audio-to-Visual Latent Alignment
Kim Sung-Bin, Arda Senocak, Hyunwoo Ha +2
How does audio describe the world around us? In this paper, we propose a method for generating an image of a scene from sound. Our method addresses the challenges of dealing with t…