The automatic recognition of a person's emotional state has become a very active research field that involves scientists specialized in different areas such as artificial intelligence, computer vision or psychology, among others. Our main objective in this work is to develop a novel approach, using persistent entropy and neural networks as main tools, to recognise and classify emotions from talking-face videos. Specifically, we combine audio-signal and image-sequence information to compute a topology signature(a 9-dimensional vector) for each video. We prove that small changes in the video produce small changes in the signature. These topological signatures are used to feed a neural network to distinguish between the following emotions: neutral, calm, happy, sad, angry, fearful, disgust, and surprised. The results reached are promising and competitive, beating the performance reached in other state-of-the-art works found in the literature.
Nanjing University（南京大学）；The Chinese University of Hong Kong（香港中文大学）；University of Sydney（悉尼大学）；Monash University（莫纳什大学）
Although significant progress has been made to audio-driven talking face generation, existing methods either neglect facial emotion or cannot be applied to arbitrary subjects. In this paper, we propose the Emotion-Aware Motion Model (EAMM) to generate one-shot emotional talking faces by involving an emotion source video. Specifically, we first propose an Audio2Facial-Dynamics module, which renders talking faces from audio-driven unsupervised zero- and first-order key-points motion. Then through exploring the motion model's properties, we further propose an Implicit Emotion Displacement Learner…
Carnegie Mellon University（卡内基梅隆大学）；National Institute of Informatics（国立信息学研究所）；A*STAR（新加坡科技研究局）
Several works have developed end-to-end pipelines for generating lip-synced talking faces with various real-world applications, such as teaching and language translation in videos. However, these prior works fail to create realistic-looking videos since they focus little on people's expressions and emotions. Moreover, these methods' effectiveness largely depends on the faces in the training dataset, which means they may not perform well on unseen faces. To mitigate this, we build a talking face generation framework conditioned on a categorical emotion to generate videos with appropriate expres…
Visual emotion expression plays an important role in audiovisual speech communication. In this work, we propose a novel approach to rendering visual emotion expression in speech-driven talking face generation. Specifically, we design an end-to-end talking face generation system that takes a speech utterance, a single face image, and a categorical emotion label as input to render a talking face video synchronized with the speech and expressing the conditioned emotion. Objective evaluation on image quality, audiovisual synchronization, and visual emotion expression shows that the proposed system…
SPEAK: Speech-Driven Pose and Emotion-Adjustable Talking Head Generation
Ping An Technology（平安科技）；Southeast University（东南大学）；Sun Yat-sen University（中山大学）；Nanjing University of Science and Technology（南京理工大学）；East China University of Science and Technology（华东理工大学）；University of Shanghai for Science and Technology（上海理工大学）
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 dataset, hence they fail to generalize to arbitrary unseen faces. In this paper, we propose a one-shot facial geometry-aware emotional talking face generation method that can generalize to arbitrary faces. We propose a graph convolutional neural network that uses speech content feature, along with an independent emotion input to generate emotion and speech-induc…
EmoGene: Audio-Driven Emotional 3D Talking-Head Generation
Khoury College of Computer Science, Northeastern University, USA（东北大学Khoury计算机科学学院）；Department of Electrical and Computer Engineering, Northeastern University, USA（东北大学电气与计算机工程系）
Geometry-guided Emotion Modulation for Controllable and Photorealistic Emotional Talking Face Generation
School of Software Technology, Zhejiang University（浙江大学软件学院）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；Ningbo Global Innovation Center, Zhejiang University（浙江大学宁波全球创新中心）
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）
Impressive progress has been made in audio-driven 3D facial animation recently, but synthesizing 3D talking-head with rich emotion is still unsolved. This is due to the lack of 3D generative models and available 3D emotional dataset with synchronized audios. To address this, we introduce 3D-TalkEmo, a deep neural network that generates 3D talking head animation with various emotions. We also create a large 3D dataset with synchronized audios and videos, rich corpus, as well as various emotion states of different persons with the sophisticated 3D face reconstruction methods. In the emotion gene…
EmoSpeaker: One-shot Fine-grained Emotion-Controlled Talking Face Generation
School of Computer Science and Technology, Xidian University（西安电子科技大学计算机科学与技术学院）；Xi’an Key Laboratory of Big Data and Intelligent Vision, Xidian University（西安电子科技大学西安大数据与智能视觉重点实验室）；Department of Computer and Information Science, University of Macau（澳门大学计算机与信息科学系）
AUHead: Realistic Emotional Talking Head Generation via Action Units Control
University of the Chinese Academy of Sciences（中国科学院大学）；National University of Singapore（新加坡国立大学）；Zhejiang University（浙江大学）；State Key Laboratory of Communication Content Cognition, People’s Daily Online（人民日報網通信內容認知重點實驗室）
TalkingGaussian: Structure-Persistent 3D Talking Head Synthesis via Gaussian Splatting
Beihang University（北京航空航天大学）；Institute of Semiconductors, Chinese Academy of Sciences（中国科学院半导体研究所）；Griffith University（格里菲斯大学）；RIKEN AIP（理化学研究所人工智能项目）；The University of Tokyo（东京大学）
Learning Disentangled Speech- and Expression-Driven Blendshapes for 3D Talking Face Animation
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Huadian (Beijing) Co-Generation Co., Ltd.（华电（北京）热电有限公司）
Netease Fuxi AI Lab（网易伏羲人工智能实验室）；University of Technology Sydney（悉尼科技大学）
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 specific, our framework consists of a speaker-independent stage and a speaker-specific stage. In the speaker-independent stage, we design three parallel networks to generate animation parameters of the mouth, upper face, and head from texts, separately. In the speaker-specific stage, we present a 3D face model guided attention network to synthesize videos tailor…
PC-Talk: Precise Facial Animation Control for Audio-Driven Talking Face Generation
MAIS, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所MAIS部）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Psyche AI.INC（Psyche AI公司）；HKUST（香港科技大学）；CAIR, HKISI, Chinese Academy of Sciences（中国科学院计算智能研究所）；SCSE, FIE, M.U.S.T（M.U.S.T的SCSE、FIE部门）
EmoTalk: Speech-Driven Emotional Disentanglement for 3D Face Animation
Renmin University of China（中国人民大学）；Nanjing University of Science and Technology（南京理工大学）；The Hong Kong University of Science and Technology（香港科技大学）；Psyche AI Inc.（赛琪人工智能公司）；Chinese Academy of Sciences（中国科学院）；Tsinghua University（清华大学）
All previous methods for audio-driven talking head generation assume the input audio to be clean with a neutral tone. As we show empirically, one can easily break these systems by simply adding certain background noise to the utterance or changing its emotional tone (to such as sad). To make talking head generation robust to such variations, we propose an explicit audio representation learning framework that disentangles audio sequences into various factors such as phonetic content, emotional tone, background noise and others. We conduct experiments to validate that conditioned on disentangled…
TalkCLIP: Talking Head Generation with Text-Guided Expressive Speaking Styles
Tsinghua University（清华大学）；BNRist（北京信息科学与技术国家研究中心）；State Key Laboratory of Intelligent Technology and Systems（智能技术与系统国家重点实验室）；THUAI（清华大学人工智能国际研究院）；Fuxi AI Lab（伏羲人工智能实验室）；Netease（网易公司）；School of Computer Science, the University of Queensland（昆士兰大学计算机科学学院）
Department of Computer Science, Rutgers University（罗格斯大学计算机科学系）
This paper presents Generative Adversarial Talking Head (GATH), a novel deep generative neural network that enables fully automatic facial expression synthesis of an arbitrary portrait with continuous action unit (AU) coefficients. Specifically, our model directly manipulates image pixels to make the unseen subject in the still photo express various emotions controlled by values of facial AU coefficients, while maintaining her personal characteristics, such as facial geometry, skin color and hair style, as well as the original surrounding background. In contrast to prior work, GATH is purely d…
Nanjing University（南京大学）；The Chinese University of Hong Kong（香港中文大学）；The University of Sydney（悉尼大学）；SenseTime Research（商汤科技研究院）；Nanyang Technological University（南洋理工大学）；Tsinghua University（清华大学）
Despite previous success in generating audio-driven talking heads, most of the previous studies focus on the correlation between speech content and the mouth shape. Facial emotion, which is one of the most important features on natural human faces, is always neglected in their methods. In this work, we present Emotional Video Portraits (EVP), a system for synthesizing high-quality video portraits with vivid emotional dynamics driven by audios. Specifically, we propose the Cross-Reconstructed Emotion Disentanglement technique to decompose speech into two decoupled spaces, i.e., a duration-indep…
Xidian University（西安电子科技大学）；Tencent AI Lab（腾讯人工智能实验室）；Tsinghua University（清华大学）
We present VideoReTalking, a new system to edit the faces of a real-world talking head video according to input audio, producing a high-quality and lip-syncing output video even with a different emotion. Our system disentangles this objective into three sequential tasks: (1) face video generation with a canonical expression; (2) audio-driven lip-sync; and (3) face enhancement for improving photo-realism. Given a talking-head video, we first modify the expression of each frame according to the same expression template using the expression editing network, resulting in a video with the canonical…
