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Recently audio-driven talking face video generation has attracted considerable attention. However, very few researches address the issue of emotional editing of these talking face videos with continuously controllable expressions, which is…

Computer Vision and Pattern Recognition · Computer Science 2023-11-29 Zhiyao Sun , Yu-Hui Wen , Tian Lv , Yanan Sun , Ziyang Zhang , Yaoyuan Wang , Yong-Jin Liu

Talking head generation is to generate video based on a given source identity and target motion. However, current methods face several challenges that limit the quality and controllability of the generated videos. First, the generated face…

Computer Vision and Pattern Recognition · Computer Science 2023-11-03 Yue Gao , Yuan Zhou , Jinglu Wang , Xiao Li , Xiang Ming , Yan Lu

Despite the significant progress in recent years, very few of the AI-based talking face generation methods attempt to render natural emotions. Moreover, the scope of the methods is majorly limited to the characteristics of the training…

Computer Vision and Pattern Recognition · Computer Science 2022-05-04 Sanjana Sinha , Sandika Biswas , Ravindra Yadav , Brojeshwar Bhowmick

The recent advances in deep learning have made it possible to generate photo-realistic images by using neural networks and even to extrapolate video frames from an input video clip. In this paper, for the sake of both furthering this…

Computer Vision and Pattern Recognition · Computer Science 2018-08-10 Lijie Fan , Wenbing Huang , Chuang Gan , Junzhou Huang , Boqing Gong

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

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 this paper, we propose a novel text-based talking-head video generation framework that synthesizes high-fidelity facial expressions and head motions in accordance with contextual sentiments as well as speech rhythm and pauses. To be…

Computer Vision and Pattern Recognition · Computer Science 2021-05-10 Lincheng Li , Suzhen Wang , Zhimeng Zhang , Yu Ding , Yixing Zheng , Xin Yu , Changjie Fan

Audio-driven talking face generation has garnered significant interest within the domain of digital human research. Existing methods are encumbered by intricate model architectures that are intricately dependent on each other, complicating…

Computer Vision and Pattern Recognition · Computer Science 2024-11-08 Dong Zhao , Jiaying Shi , Wenjun Li , Shudong Wang , Shenghui Xu , Zhaoming Pan

Real-world talking faces often accompany with natural head movement. However, most existing talking face video generation methods only consider facial animation with fixed head pose. In this paper, we address this problem by proposing a…

Computer Vision and Pattern Recognition · Computer Science 2020-03-06 Ran Yi , Zipeng Ye , Juyong Zhang , Hujun Bao , Yong-Jin Liu

Production-level workflows for producing convincing 3D dynamic human faces have long relied on an assortment of labor-intensive tools for geometry and texture generation, motion capture and rigging, and expression synthesis. Recent neural…

Computer Vision and Pattern Recognition · Computer Science 2022-09-19 Longwen Zhang , Chuxiao Zeng , Qixuan Zhang , Hongyang Lin , Ruixiang Cao , Wei Yang , Lan Xu , Jingyi Yu

Multimodal-driven talking face generation refers to animating a portrait with the given pose, expression, and gaze transferred from the driving image and video, or estimated from the text and audio. However, existing methods ignore the…

Computer Vision and Pattern Recognition · Computer Science 2023-05-10 Chao Xu , Shaoting Zhu , Junwei Zhu , Tianxin Huang , Jiangning Zhang , Ying Tai , Yong Liu

It is in high demand to generate facial animation with high realism, but it remains a challenging task. Existing approaches of speech-driven facial animation can produce satisfactory mouth movement and lip synchronization, but show weakness…

Computer Vision and Pattern Recognition · Computer Science 2025-05-08 Yutong Chen , Junhong Zhao , Wei-Qiang Zhang

In this paper, we consider a novel and practical case for talking face video generation. Specifically, we focus on the scenarios involving multi-people interactions, where the talking context, such as audience or surroundings, is present.…

Computer Vision and Pattern Recognition · Computer Science 2024-02-29 Meidai Xuanyuan , Yuwang Wang , Honglei Guo , Qionghai Dai

Recent diffusion-based talking face generation models have demonstrated impressive potential in synthesizing videos that accurately match a speech audio clip with a given reference identity. However, existing approaches still encounter…

Computer Vision and Pattern Recognition · Computer Science 2025-10-16 Xingpei Ma , Jiaran Cai , Yuansheng Guan , Shenneng Huang , Qiang Zhang , Shunsi Zhang

We introduce a novel approach for high-resolution talking head generation from a single image and audio input. Prior methods using explicit face models, like 3D morphable models (3DMM) and facial landmarks, often fall short in generating…

Computer Vision and Pattern Recognition · Computer Science 2024-12-11 Sejong Yang , Seoung Wug Oh , Yang Zhou , Seon Joo Kim

Given an arbitrary face image and an arbitrary speech clip, the proposed work attempts to generating the talking face video with accurate lip synchronization while maintaining smooth transition of both lip and facial movement over the…

Computer Vision and Pattern Recognition · Computer Science 2019-07-29 Yang Song , Jingwen Zhu , Dawei Li , Xiaolong Wang , Hairong Qi

Talking Face Generation (TFG) strives to create realistic and emotionally expressive digital faces. While previous TFG works have mastered the creation of naturalistic facial movements, they typically express a fixed target emotion in…

Computer Vision and Pattern Recognition · Computer Science 2026-03-09 Hao Yang , Yanyan Zhao , Tian Zheng , Hongbo Zhang , Bichen Wang , Di Wu , Xing Fu , Xuda Zhi , Yongbo Huang , Hao He

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

The goal of this paper is to synthesise talking faces with controllable facial motions. To achieve this goal, we propose two key ideas. The first is to establish a canonical space where every face has the same motion patterns but different…

Computer Vision and Pattern Recognition · Computer Science 2023-09-19 Youngjoon Jang , Kyeongha Rho , Jong-Bin Woo , Hyeongkeun Lee , Jihwan Park , Youshin Lim , Byeong-Yeol Kim , Joon Son Chung

Traditional 3D content creation tools empower users to bring their imagination to life by giving them direct control over a scene's geometry, appearance, motion, and camera path. Creating computer-generated videos, however, is a tedious…

Computer Vision and Pattern Recognition · Computer Science 2023-12-05 Shengqu Cai , Duygu Ceylan , Matheus Gadelha , Chun-Hao Paul Huang , Tuanfeng Yang Wang , Gordon Wetzstein
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