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Related papers: IMTalker: Efficient Audio-driven Talking Face Gene…

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Generating semantically coherent and visually accurate talking faces requires bridging the gap between linguistic meaning and facial articulation. Although audio-driven methods remain prevalent, their reliance on high-quality paired audio…

Computer Vision and Pattern Recognition · Computer Science 2025-08-05 Xu Wang , Shengeng Tang , Fei Wang , Lechao Cheng , Dan Guo , Feng Xue , Richang Hong

Long-duration talking video synthesis faces enduring challenges in achieving high video quality, portrait consistency, temporal coherence, and computational efficiency. As video length increases, issues such as visual degradation, portrait…

Computer Vision and Pattern Recognition · Computer Science 2026-05-11 Haojie Zhang , Zhihao Liang , Ruibo Fu , Bingyan Liu , Zhengqi Wen , Xuefei Liu , Jianhua Tao , Yaling Liang

Recent advances in talking face generation have significantly improved facial animation synthesis. However, existing approaches face fundamental limitations: 3DMM-based methods maintain temporal consistency but lack fine-grained regional…

Computer Vision and Pattern Recognition · Computer Science 2025-03-26 Kangwei Liu , Junwu Liu , Yun Cao , Jinlin Guo , Xiaowei Yi

Audio-driven talking-head generation is a crucial and useful technology for virtual human interaction and film-making. While recent advances have focused on improving image fidelity and lip synchronization, generating accurate emotional…

Computer Vision and Pattern Recognition · Computer Science 2025-05-05 Wenqing Wang , Yun Fu

Conventional GAN-based models for talking head generation often suffer from limited quality and unstable training. Recent approaches based on diffusion models aimed to address these limitations and improve fidelity. However, they still face…

Computer Vision and Pattern Recognition · Computer Science 2024-03-29 Seyeon Kim , Siyoon Jin , Jihye Park , Kihong Kim , Jiyoung Kim , Jisu Nam , Seungryong Kim

Recently, emotional talking face generation has received considerable attention. However, existing methods only adopt one-hot coding, image, or audio as emotion conditions, thus lacking flexible control in practical applications and failing…

Computer Vision and Pattern Recognition · Computer Science 2023-06-01 Chao Xu , Junwei Zhu , Jiangning Zhang , Yue Han , Wenqing Chu , Ying Tai , Chengjie Wang , Zhifeng Xie , Yong Liu

Generating naturalistic and nuanced listener motions for extended interactions remains an open problem. Existing methods often rely on low-dimensional motion codes for facial behavior generation followed by photorealistic rendering,…

Computer Vision and Pattern Recognition · Computer Science 2025-04-08 Maksim Siniukov , Di Chang , Minh Tran , Hongkun Gong , Ashutosh Chaubey , Mohammad Soleymani

In this paper, we abstract the process of people hearing speech, extracting meaningful cues, and creating various dynamically audio-consistent talking faces, termed Listening and Imagining, into the task of high-fidelity diverse talking…

Computer Vision and Pattern Recognition · Computer Science 2024-04-02 Chao Xu , Yang Liu , Jiazheng Xing , Weida Wang , Mingze Sun , Jun Dan , Tianxin Huang , Siyuan Li , Zhi-Qi Cheng , Ying Tai , Baigui Sun

Since the beginning of the COVID-19 pandemic, remote conferencing and school-teaching have become important tools. The previous applications aim to save the commuting cost with real-time interactions. However, our application is going to…

Artificial Intelligence · Computer Science 2022-10-14 Aolan Sun , Xulong Zhang , Tiandong Ling , Jianzong Wang , Ning Cheng , Jing Xiao

This paper investigates a novel task of talking face video generation solely from speeches. The speech-to-video generation technique can spark interesting applications in entertainment, customer service, and human-computer-interaction…

Sound · Computer Science 2021-07-15 Shijing Si , Jianzong Wang , Xiaoyang Qu , Ning Cheng , Wenqi Wei , Xinghua Zhu , Jing Xiao

Controllability, generalizability and efficiency are the major objectives of constructing face avatars represented by neural implicit field. However, existing methods have not managed to accommodate the three requirements simultaneously.…

Computer Vision and Pattern Recognition · Computer Science 2023-03-28 Zhiyuan Ma , Xiangyu Zhu , Guojun Qi , Zhen Lei , Lei Zhang

The generation of stylistic 3D facial animations driven by speech presents a significant challenge as it requires learning a many-to-many mapping between speech, style, and the corresponding natural facial motion. However, existing methods…

Computer Vision and Pattern Recognition · Computer Science 2024-05-15 Zhiyao Sun , Tian Lv , Sheng Ye , Matthieu Lin , Jenny Sheng , Yu-Hui Wen , Minjing Yu , Yong-Jin Liu

Audio-driven 3D facial animation aims to generate synchronized lip movements and vivid facial expressions from arbitrary audio clips. While existing methods can produce synchronized lip motions, they often rely on predefined identity or…

Computer Vision and Pattern Recognition · Computer Science 2026-05-29 Xuangeng Chu , Yuan Gan , Ziteng Cui , Shuhong Liu , Jian Wang , Bing Zhou , Tatsuya Harada

Speech-driven talking heads have recently emerged and enable interactive avatars. However, real-world applications are limited, as current methods achieve high visual fidelity but slow or fast yet temporally unstable. Diffusion methods…

Computer Vision and Pattern Recognition · Computer Science 2025-12-12 Madhav Agarwal , Mingtian Zhang , Laura Sevilla-Lara , Steven McDonagh

Speech-driven 3D facial animation is important for many multimedia applications. Recent work has shown promise in using either Diffusion models or Transformer architectures for this task. However, their mere aggregation does not lead to…

Computer Vision and Pattern Recognition · Computer Science 2024-02-09 Zhiyuan Ma , Xiangyu Zhu , Guojun Qi , Chen Qian , Zhaoxiang Zhang , Zhen Lei

We present MagicInfinite, a novel diffusion Transformer (DiT) framework that overcomes traditional portrait animation limitations, delivering high-fidelity results across diverse character types-realistic humans, full-body figures, and…

Computer Vision and Pattern Recognition · Computer Science 2025-03-11 Hongwei Yi , Tian Ye , Shitong Shao , Xuancheng Yang , Jiantong Zhao , Hanzhong Guo , Terrance Wang , Qingyu Yin , Zeke Xie , Lei Zhu , Wei Li , Michael Lingelbach , Daquan Zhou

Individuals have unique facial expression and head pose styles that reflect their personalized speaking styles. Existing one-shot talking head methods cannot capture such personalized characteristics and therefore fail to produce diverse…

Computer Vision and Pattern Recognition · Computer Science 2024-09-17 Suzhen Wang , Yifeng Ma , Yu Ding , Zhipeng Hu , Changjie Fan , Tangjie Lv , Zhidong Deng , Xin Yu

This paper introduces Stereo-Talker, a novel one-shot audio-driven human video synthesis system that generates 3D talking videos with precise lip synchronization, expressive body gestures, temporally consistent photo-realistic quality, and…

Computer Vision and Pattern Recognition · Computer Science 2026-03-02 Xiang Deng , Youxin Pang , Xiaochen Zhao , Chao Xu , Lizhen Wang , Hongjiang Xiao , Shi Yan , Hongwen Zhang , Yebin Liu

Recently, talking-face video generation has received considerable attention. So far most methods generate results with neutral expressions or expressions that are implicitly determined by neural networks in an uncontrollable way. In this…

Computer Vision and Pattern Recognition · Computer Science 2022-04-14 Zipeng Ye , Zhiyao Sun , Yu-Hui Wen , Yanan Sun , Tian Lv , Ran Yi , Yong-Jin Liu

Diffusion models have revolutionized the field of talking head generation, yet still face challenges in expressiveness, controllability, and stability in long-time generation. In this research, we propose an EmotiveTalk framework to address…

Computer Vision and Pattern Recognition · Computer Science 2024-12-17 Haotian Wang , Yuzhe Weng , Yueyan Li , Zilu Guo , Jun Du , Shutong Niu , Jiefeng Ma , Shan He , Xiaoyan Wu , Qiming Hu , Bing Yin , Cong Liu , Qingfeng Liu