arXiv:2610.22499v1 Announce Type: new
Abstract: Human communication is inherently multimodal, conveying intent not only through linguistic and vocal cues but also through visual signals. However, exi...
By Junseok Ahn, Jaehyun Park, Jeongsoo Choi, Joon Son Chung, Ji-Hoon Kim
arXiv:2609.10394v1 Announce Type: cross
Abstract: Current audio-visual speech recognition (AVSR) benchmarks, like LRS3, rely heavily on clean, scripted and rehearsed speech. They fail to reflect the...
By Rishabh Jain, Aristeidis Papadopoulos, Zhaofeng Lin, Naomi Harte
arXiv:2606. 30145v1 Announce Type: new Abstract: Natural face-to-face conversation requires real-time speech generation together with synchronized facial motion.
By Habin Lim, Jae-Ho Lee, Hah Min Lew, Ji-Su Kang, Gyeong-Moon Park
Lipreading remains challenging due to the inherent ambiguity of visual speech cues and their sensitivity to head pose and video resolution. In this work, we propose FLARE, a framework for robust visua...
arXiv:2610.19559v1 Announce Type: new
Abstract: Lipreading remains challenging due to the inherent ambiguity of visual speech cues and their sensitivity to head pose and video resolution. In this wor...
By Jing-Xuan Zhang, Genshun Wan, Jianqing Gao, Jia Pan, Yumei Zhang, Lichen Zhang
Natural face-to-face conversation requires real-time speech generation together with synchronized facial motion. Existing systems only partially address this problem: speech-only full-duplex models can generate speech in real time but do not produce facial motion, while audio-driven facial motion models animate a face from already available audio rather than jointly generating speech and motion online.