Current talking face generation methods mainly focus on speech-lip synchronization. However, insufficient investigation on the facial talking style leads to a lifeless and monotonous avatar. Most previous works fail to imitate expressive styles from arbitrary video prompts and ensure the authenticity of the generated video. This paper proposes an unsupervised variational style transfer model (VAST) to vivify the neutral photo-realistic avatars. Our model consists of three key components: a style encoder that extracts facial style representations from the given video prompts; a hybrid facial expression decoder to model accurate speech-related movements; a variational style enhancer that enhances the style space to be highly expressive and meaningful. With our essential designs on facial style learning, our model is able to flexibly capture the expressive facial style from arbitrary video prompts and transfer it onto a personalized image renderer in a zero-shot manner. Experimental results demonstrate the proposed approach contributes to a more vivid talking avatar with higher authenticity and richer expressiveness.
VedicTHG: Symbolic Vedic Computation for Low-Resource Talking-Head Generation in Educational Avatars
Engineering Sciences, Homi Bhabha National Institute Training School Complex（工程科学，霍米·巴哈瓦国家研究所培训学校复杂区）；Computer and Informatics Group, Variable Energy Cyclotron Centre（计算机与信息组，变能回旋加速器中心）
TurboTalk: Progressive Distillation for One-Step Audio-Driven Talking Avatar 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）；Meituan（美团）；The Hong Kong Polytechnic University（香港理工大学）；CAIR, HKISI, CAS（中国科学院CAS HKISI CAIR）
MAGIC-Talk: Motion-aware Audio-Driven Talking Face Generation with Customizable Identity Control
School of Computer Science and Electronic Engineering, University of Surrey（Surrey大学计算机科学与电子工程学院）；School of Artificial Intelligence and Computer Science, Jiangnan University（江南大学人工智能与计算机科学学院）；Centre for Vision, Speech and Signal Processing, University of Surrey（Surrey大学视觉、语音和信号处理中心）
AvatarForcing: One-Step Streaming Talking Avatars via Local-Future Sliding-Window Denoising
Zhejiang University（浙江大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；Kling Team, Kuaishou Technology（快手科技 Kling 团队）；Tsinghua University（清华大学）
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）
FaceEditTalker: Controllable Talking Head Generation with Facial Attribute Editing
Xi’an Key Laboratory of Big Data and Intelligent Vision, Xidian University, Xi’an 710071, China（西安大数据与智能视觉重点实验室，西安电子科技大学，西安710071，中国）；Key Laboratory of Collaborative Intelligence Systems, Ministry of Education, Xidian University, Xi’an 710071, China（协同智能系统重点实验室，教育部，西安电子科技大学，西安710071，中国）；School of Computer Science and Technology, Xidian University, Xi’an 710071, China（计算机科学与技术学院，西安电子科技大学，西安710071，中国）
The Hong Kong Polytechnic University（香港理工大学）；Center for Artificial Intelligence and Robotics, HKISI CAS（中国科学院香港创新研究院人工智能与机器人创新中心）；State Key Laboratory of Multimodal Artificial Intelligence Systems, CASIA（中国科学院自动化研究所多模态人工智能系统全国重点实验室）；OPPO Research（OPPO研究院）
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. They either focus on static portraits, restricting the representation ability to a specific subject, or suffer from substantial computational cost, limiting their flexibility. In this paper, we propose One-shot Talking face Avatar (OTAvatar), which constructs face avatars by a generalized controllable tri-plane rendering solution so that each personalized avat…
DEGAS: Detailed Expressions on Full-Body Gaussian Avatars
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Prometheus Vision Technology Co., Ltd.（天视智能科技有限公司）；The Hong Kong University of Science and Technology（香港科技大学）；Swinburne University of Technology（斯威本科技大学）
Tsinghua University（清华大学）；Beijing National Research Center for Information Science and Technology（北京信息科学与技术国家研究中心）；PhiGent Robotics（鉴智机器人）
Talking head synthesis is an emerging technology with wide applications in film dubbing, virtual avatars and online education. Recent NeRF-based methods generate more natural talking videos, as they better capture the 3D structural information of faces. However, a specific model needs to be trained for each identity with a large dataset. In this paper, we propose Dynamic Facial Radiance Fields (DFRF) for few-shot talking head synthesis, which can rapidly generalize to an unseen identity with few training data. Different from the existing NeRF-based methods which directly encode the 3D geometry…
Technical University of Munich（慕尼黑工业大学）；Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）；Saarland Informatics Campus（萨尔兰信息学园区）
We present Neural Voice Puppetry, a novel approach for audio-driven facial video synthesis. Given an audio sequence of a source person or digital assistant, we generate a photo-realistic output video of a target person that is in sync with the audio of the source input. This audio-driven facial reenactment is driven by a deep neural network that employs a latent 3D face model space. Through the underlying 3D representation, the model inherently learns temporal stability while we leverage neural rendering to generate photo-realistic output frames. Our approach generalizes across different peopl…
ALIVE: An Avatar-Lecture Interactive Video Engine with Content-Aware Retrieval for Real-Time Interaction
Department of Computer Science Rensselaer Polytechnic Institute（计算机科学系罗切斯特理工学院）；Department of Biomedical Engineering Rensselaer Polytechnic Institute（生物医学工程系罗切斯特理工学院）
