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One-shot talking head generation has no explicit head movement reference, thus it is difficult to generate talking heads with head motions. Some existing works only edit the mouth area and generate still talking heads, leading to unreal…

Computer Vision and Pattern Recognition · Computer Science 2023-09-29 Jin Liu , Xi Wang , Xiaomeng Fu , Yesheng Chai , Cai Yu , Jiao Dai , Jizhong Han

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

While the recent advances in research on video reenactment have yielded promising results, the approaches fall short in capturing the fine, detailed, and expressive facial features (e.g., lip-pressing, mouth puckering, mouth gaping, and…

Computer Vision and Pattern Recognition · Computer Science 2023-02-15 Trevine Oorloff , Yaser Yacoob

Audio-driven talking-head generation has advanced rapidly with diffusion-based generative models, yet producing temporally coherent videos with fine-grained motion control remains challenging. We propose DEMO, a flow-matching generative…

Computer Vision and Pattern Recognition · Computer Science 2025-10-14 Peiyin Chen , Zhuowei Yang , Hui Feng , Sheng Jiang , Rui Yan

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

Speech-driven animation has gained significant traction in recent years, with current methods achieving near-photorealistic results. However, the field remains underexplored regarding non-verbal communication despite evidence demonstrating…

Computer Vision and Pattern Recognition · Computer Science 2023-08-31 Antoni Bigata Casademunt , Rodrigo Mira , Nikita Drobyshev , Konstantinos Vougioukas , Stavros Petridis , Maja Pantic

Talking face generation has gained significant attention as a core application of generative models. To enhance the expressiveness and realism of synthesized videos, emotion editing in talking face video plays a crucial role. However,…

Computer Vision and Pattern Recognition · Computer Science 2026-04-20 Chanhyuk Choi , Taesoo Kim , Donggyu Lee , Siyeol Jung , Taehwan Kim

Contemporary models for generating images show remarkable quality and versatility. Swayed by these advantages, the research community repurposes them to generate videos. Since video content is highly redundant, we argue that naively…

Computer Vision and Pattern Recognition · Computer Science 2024-02-23 Willi Menapace , Aliaksandr Siarohin , Ivan Skorokhodov , Ekaterina Deyneka , Tsai-Shien Chen , Anil Kag , Yuwei Fang , Aleksei Stoliar , Elisa Ricci , Jian Ren , Sergey Tulyakov

The generation of temporally consistent, high-fidelity driving videos over extended horizons presents a fundamental challenge in autonomous driving world modeling. Existing approaches often suffer from error accumulation and feature…

Computer Vision and Pattern Recognition · Computer Science 2025-09-03 Jiamin Wang , Yichen Yao , Xiang Feng , Hang Wu , Yaming Wang , Qingqiu Huang , Yuexin Ma , Xinge Zhu

In the era of digital animation, the quest to produce lifelike facial animations for virtual characters has led to the development of various retargeting methods. While the retargeting facial motion between models of similar shapes has been…

Computer Vision and Pattern Recognition · Computer Science 2026-01-14 Yeonsoo Choi , Inyup Lee , Sihun Cha , Seonghyeon Kim , Sunjin Jung , Junyong Noh

Almost all advanced face swapping approaches use reconstruction as the proxy task, i.e., supervision only exists when the target and source belong to the same person. Otherwise, lacking pixel-level supervision, these methods struggle for…

Computer Vision and Pattern Recognition · Computer Science 2023-06-09 Ge Yuan , Maomao Li , Yong Zhang , Huicheng Zheng

Head avatar reenactment focuses on creating animatable personal avatars from monocular videos, serving as a foundational element for applications like social signal understanding, gaming, human-machine interaction, and computer vision.…

Computer Vision and Pattern Recognition · Computer Science 2026-01-28 Wei Liang , Hui Yu , Derui Ding , Rachael E. Jack , Philippe G. Schyns

Visual content has become the primary source of information, as evident in the billions of images and videos, shared and uploaded on the Internet every single day. This has led to an increase in alterations in images and videos to make them…

Computer Vision and Pattern Recognition · Computer Science 2020-01-22 Prabhat Kumar , Mayank Vatsa , Richa Singh

Whenever we speak, our voice is accompanied by facial movements and expressions. Several recent works have shown the synthesis of highly photo-realistic videos of talking faces, but they either require a source video to drive the target…

Computer Vision and Pattern Recognition · Computer Science 2020-11-03 Prateek Manocha , Prithwijit Guha

Current audio-driven facial animation methods achieve impressive results for short videos but suffer from error accumulation and identity drift when extended to longer durations. Existing methods attempt to mitigate this through external…

High-fidelity and efficient audio-driven talking head generation has been a key research topic in computer graphics and computer vision. In this work, we study vector image based audio-driven talking head generation. Compared with directly…

Computer Vision and Pattern Recognition · Computer Science 2023-12-20 Hao Hu , Xuan Wang , Jingxiang Sun , Yanbo Fan , Yu Guo , Caigui Jiang

Audio-driven talking head generation is advancing from 2D to 3D content. Notably, Neural Radiance Field (NeRF) is in the spotlight as a means to synthesize high-quality 3D talking head outputs. Unfortunately, this NeRF-based approach…

Computer Vision and Pattern Recognition · Computer Science 2024-05-13 Gihoon Kim , Kwanggyoon Seo , Sihun Cha , Junyong Noh

Video diffusion models substantially boost the productivity of artistic workflows with high-quality portrait video generative capacity. However, prevailing pipelines are primarily constrained to single-shot creation, while real-world…

Computer Vision and Pattern Recognition · Computer Science 2025-06-23 Jiahao Wang , Hualian Sheng , Sijia Cai , Weizhan Zhang , Caixia Yan , Yachuang Feng , Bing Deng , Jieping Ye

End-to-end human animation, such as audio-driven talking human generation, has undergone notable advancements in the recent few years. However, existing methods still struggle to scale up as large general video generation models, limiting…

Computer Vision and Pattern Recognition · Computer Science 2025-07-01 Gaojie Lin , Jianwen Jiang , Jiaqi Yang , Zerong Zheng , Chao Liang

Recent works have shown how realistic talking face images can be obtained under the supervision of geometry guidance, e.g., facial landmark or boundary. To alleviate the demand for manual annotations, in this paper, we propose a novel…

Computer Vision and Pattern Recognition · Computer Science 2020-03-31 Xianfang Zeng , Yusu Pan , Mengmeng Wang , Jiangning Zhang , Yong Liu