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Talking face generation technology creates talking videos from arbitrary appearance and motion signal, with the "arbitrary" offering ease of use but also introducing challenges in practical applications. Existing methods work well with…

Computer Vision and Pattern Recognition · Computer Science 2024-03-27 Chao Liang , Jianwen Jiang , Tianyun Zhong , Gaojie Lin , Zhengkun Rong , Jiaqi Yang , Yongming Zhu

Although significant progress has been made in audio-driven talking head generation, text-driven methods remain underexplored. In this work, we present OmniTalker, a unified framework that jointly generates synchronized talking audio-video…

Computer Vision and Pattern Recognition · Computer Science 2025-06-04 Zhongjian Wang , Peng Zhang , Jinwei Qi , Guangyuan Wang , Chaonan Ji , Sheng Xu , Bang Zhang , Liefeng Bo

One-shot talking face generation aims at synthesizing a high-quality talking face video from an arbitrary portrait image, driven by a video or an audio segment. One challenging quality factor is the resolution of the output video: higher…

Computer Vision and Pattern Recognition · Computer Science 2022-03-18 Fei Yin , Yong Zhang , Xiaodong Cun , Mingdeng Cao , Yanbo Fan , Xuan Wang , Qingyan Bai , Baoyuan Wu , Jue Wang , Yujiu Yang

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

Audio-driven talking head generation is a core component of digital avatars, and 3D Gaussian Splatting has shown strong performance in real-time rendering of high-fidelity talking heads. However, achieving precise control over fine-grained…

Computer Vision and Pattern Recognition · Computer Science 2026-02-11 Shaoyang Xie , Xiaofeng Cong , Baosheng Yu , Zhipeng Gui , Jie Gui , Yuan Yan Tang , James Tin-Yau Kwok

Generating talking person portraits with arbitrary speech audio is a crucial problem in the field of digital human and metaverse. A modern talking face generation method is expected to achieve the goals of generalized audio-lip…

Computer Vision and Pattern Recognition · Computer Science 2023-05-02 Zhenhui Ye , Jinzheng He , Ziyue Jiang , Rongjie Huang , Jiawei Huang , Jinglin Liu , Yi Ren , Xiang Yin , Zejun Ma , Zhou Zhao

We introduce FactorPortrait, a video diffusion method for controllable portrait animation that enables lifelike synthesis from disentangled control signals of facial expressions, head movement, and camera viewpoints. Given a single portrait…

Computer Vision and Pattern Recognition · Computer Science 2025-12-15 Jiapeng Tang , Kai Li , Chengxiang Yin , Liuhao Ge , Fei Jiang , Jiu Xu , Matthias Nießner , Christian Häne , Timur Bagautdinov , Egor Zakharov , Peihong Guo

We present a novel approach for synthesizing 3D talking heads with controllable emotion, featuring enhanced lip synchronization and rendering quality. Despite significant progress in the field, prior methods still suffer from multi-view…

Computer Vision and Pattern Recognition · Computer Science 2024-08-02 Qianyun He , Xinya Ji , Yicheng Gong , Yuanxun Lu , Zhengyu Diao , Linjia Huang , Yao Yao , Siyu Zhu , Zhan Ma , Songcen Xu , Xiaofei Wu , Zixiao Zhang , Xun Cao , Hao Zhu

Speech-driven 3D talking head generation aims to produce lifelike facial animations precisely synchronized with speech. While considerable progress has been made in achieving high lip-synchronization accuracy, existing methods largely…

Computer Vision and Pattern Recognition · Computer Science 2025-12-30 Bin Wang , Yang Xu , Huan Zhao , Hao Zhang , Zixing Zhang

Taking inspiration from recent developments in visual generative tasks using diffusion models, we propose a method for end-to-end speech-driven video editing using a denoising diffusion model. Given a video of a talking person, and a…

Computer Vision and Pattern Recognition · Computer Science 2023-05-12 Dan Bigioi , Shubhajit Basak , Michał Stypułkowski , Maciej Zięba , Hugh Jordan , Rachel McDonnell , Peter Corcoran

In this paper, we propose a talking face generation method that takes an audio signal as input and a short target video clip as reference, and synthesizes a photo-realistic video of the target face with natural lip motions, head poses, and…

Computer Vision and Pattern Recognition · Computer Science 2021-08-19 Chenxu Zhang , Yifan Zhao , Yifei Huang , Ming Zeng , Saifeng Ni , Madhukar Budagavi , Xiaohu Guo

When people deliver a speech, they naturally move heads, and this rhythmic head motion conveys prosodic information. However, generating a lip-synced video while moving head naturally is challenging. While remarkably successful, existing…

Computer Vision and Pattern Recognition · Computer Science 2020-07-20 Lele Chen , Guofeng Cui , Celong Liu , Zhong Li , Ziyi Kou , Yi Xu , Chenliang Xu

In recent years, the field of talking faces generation has attracted considerable attention, with certain methods adept at generating virtual faces that convincingly imitate human expressions. However, existing methods face challenges…

Computer Vision and Pattern Recognition · Computer Science 2024-01-17 Bingyuan Zhang , Xulong Zhang , Ning Cheng , Jun Yu , Jing Xiao , Jianzong Wang

Audio-driven talking video generation has advanced significantly, but existing methods often depend on video-to-video translation techniques and traditional generative networks like GANs and they typically generate taking heads and…

Computer Vision and Pattern Recognition · Computer Science 2024-09-13 Steven Hogue , Chenxu Zhang , Hamza Daruger , Yapeng Tian , Xiaohu Guo

Recent advances in deep learning for sequential data have given rise to fast and powerful models that produce realistic videos of talking humans. The state of the art in talking face generation focuses mainly on lip-syncing, being…

Computer Vision and Pattern Recognition · Computer Science 2023-12-12 Georgios Milis , Panagiotis P. Filntisis , Anastasios Roussos , Petros Maragos

Laughter is a unique expression, essential to affirmative social interactions of humans. Although current 3D talking head generation methods produce convincing verbal articulations, they often fail to capture the vitality and subtleties of…

Computer Vision and Pattern Recognition · Computer Science 2023-11-03 Kim Sung-Bin , Lee Hyun , Da Hye Hong , Suekyeong Nam , Janghoon Ju , Tae-Hyun Oh

Recent studies in speech-driven 3D talking head generation have achieved convincing results in verbal articulations. However, generating accurate lip-syncs degrades when applied to input speech in other languages, possibly due to the lack…

Computer Vision and Pattern Recognition · Computer Science 2024-06-21 Kim Sung-Bin , Lee Chae-Yeon , Gihun Son , Oh Hyun-Bin , Janghoon Ju , Suekyeong Nam , Tae-Hyun Oh

3D Gaussian splatting-based talking head synthesis has recently gained attention for its ability to render high-fidelity images with real-time inference speed. However, since it is typically trained on only a short video that lacks the…

Computer Vision and Pattern Recognition · Computer Science 2025-02-04 Junuk Cha , Seongro Yoon , Valeriya Strizhkova , Francois Bremond , Seungryul Baek

In this paper, we present a dynamic convolution kernel (DCK) strategy for convolutional neural networks. Using a fully convolutional network with the proposed DCKs, high-quality talking-face video can be generated from multi-modal sources…

Computer Vision and Pattern Recognition · Computer Science 2022-04-20 Zipeng Ye , Mengfei Xia , Ran Yi , Juyong Zhang , Yu-Kun Lai , Xuwei Huang , Guoxin Zhang , Yong-jin Liu

Audio-driven talking head generation is a significant and challenging task applicable to various fields such as virtual avatars, film production, and online conferences. However, the existing GAN-based models emphasize generating…

Computer Vision and Pattern Recognition · Computer Science 2024-08-06 Jintao Tan , Xize Cheng , Lingyu Xiong , Lei Zhu , Xiandong Li , Xianjia Wu , Kai Gong , Minglei Li , Yi Cai