6 papers
SVHalluc: Benchmarking Speech-Vision Hallucination in Audio-Visual Large Language Models
Chenshuang Zhang, Kyeong Seon Kim, Chengxin Liu +1
Despite the success of audio-visual large-language models (LLMs), they can produce plausible but ungrounded outputs, termed hallucination. Existing benchmarks focus on environmenta…
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…
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…
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…
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…
Aligning Sight and Sound: Advanced Sound Source Localization Through Audio-Visual Alignment
Arda Senocak, Hyeonggon Ryu, Junsik Kim +3
Recent studies on learning-based sound source localization have mainly focused on the localization performance perspective. However, prior work and existing benchmarks overlook a c…