Semantic-Aware Implicit Neural Audio-Driven Video Portrait Generation
The Chinese University of Hong Kong（香港中文大学）；Monash University（莫纳什大学）；SenseTime Research（商汤科技研究院）；Sensetime（商汤科技（此处原英文为“sensetime”，结合上下文“wuwenyan@sensetime.com”推测为“商汤科技”，但严格按“机构粒度”提取，原文本中“SenseTime Research”已单独列出，此处“sensetime”视为同一机构））
Xian Liu, Yinghao Xu, Qianyi Wu, Hang Zhou, Wayne Wu, Bolei Zhou
Animating high-fidelity video portrait with speech audio is crucial for virtual reality and digital entertainment. While most previous studies rely on accurate explicit structural information, recent works explore the implicit scene representation of Neural Radiance Fields (NeRF) for realistic generation. In order to capture the inconsistent motions as well as the semantic difference between human head and torso, some work models them via two individual sets of NeRF, leading to unnatural results. In this work, we propose Semantic-aware Speaking Portrait NeRF (SSP-NeRF), which creates delicate audio-driven portraits using one unified set of NeRF. The proposed model can handle the detailed local facial semantics and the global head-torso relationship through two semantic-aware modules. Specifically, we first propose a Semantic-Aware Dynamic Ray Sampling module with an additional parsing branch that facilitates audio-driven volume rendering. Moreover, to enable portrait rendering in one unified neural radiance field, a Torso Deformation module is designed to stabilize the large-scale non-rigid torso motions. Extensive evaluations demonstrate that our proposed approach renders more realistic video portraits compared to previous methods. Project page: https://alvinliu0.github.io/projects/SSP-NeRF
Peking University（北京大学）；The University of Sydney（悉尼大学）；Baidu Inc.（百度公司）
While dynamic Neural Radiance Fields (NeRF) have shown success in high-fidelity 3D modeling of talking portraits, the slow training and inference speed severely obstruct their potential usage. In this paper, we propose an efficient NeRF-based framework that enables real-time synthesizing of talking portraits and faster convergence by leveraging the recent success of grid-based NeRF. Our key insight is to decompose the inherently high-dimensional talking portrait representation into three low-dimensional feature grids. Specifically, a Decomposed Audio-spatial Encoding Module models the dynamic …
Audio-Driven Talking Face Generation with Blink Embedding and Hash Grid Landmarks Encoding
Department of Control Science and Engineering, University of Shanghai for Science and Technology（控制科学与工程系，上海科学技术大学）；School of Psychology and Neuroscience, University of Glasgow（心理学与神经科学学院，格拉斯哥大学）
We present a method for estimating Neural Radiance Fields (NeRF) from a single headshot portrait. While NeRF has demonstrated high-quality view synthesis, it requires multiple images of static scenes and thus impractical for casual captures and moving subjects. In this work, we propose to pretrain the weights of a multilayer perceptron (MLP), which implicitly models the volumetric density and colors, with a meta-learning framework using a light stage portrait dataset. To improve the generalization to unseen faces, we train the MLP in the canonical coordinate space approximated by 3D face morph…
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）
AE-NeRF: Audio Enhanced Neural Radiance Field for Few Shot Talking Head Synthesis
University of Chinese Academy of Sciences（中国科学院大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Alibaba Group（阿里巴巴集团）；Nanjing University（南京大学）
Generating photo-realistic video portrait with arbitrary speech audio is a crucial problem in film-making and virtual reality. Recently, several works explore the usage of neural radiance field in this task to improve 3D realness and image fidelity. However, the generalizability of previous NeRF-based methods to out-of-domain audio is limited by the small scale of training data. In this work, we propose GeneFace, a generalized and high-fidelity NeRF-based talking face generation method, which can generate natural results corresponding to various out-of-domain audio. Specifically, we learn a va…
ShanghaiTech University（上海科技大学）；Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences（中国科学院上海微系统与信息技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Shanghai Engineering Research Center of Intelligent Vision and Imaging（上海智能视觉与成像工程技术研究中心）
Photo-realistic neural reconstruction and rendering of the human portrait are critical for numerous VR/AR applications. Still, existing solutions inherently rely on multi-view capture settings, and the one-shot solution to get rid of the tedious multi-view synchronization and calibration remains extremely challenging. In this paper, we propose MirrorNeRF - a one-shot neural portrait free-viewpoint rendering approach using a catadioptric imaging system with multiple sphere mirrors and a single high-resolution digital camera, which is the first to combine neural radiance field with catadioptric …
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…
PortraitTalk: Towards Customizable One-Shot Audio-to-Talking Face Generation
School of Computer Science and Electronic Engineering, University of Surrey（计算机科学与电子工程系，塞夫顿大学）；Centre for Vision, Speech and Signal Processing, University of Surrey（视觉、语音与信号处理中心，塞夫顿大学）；School of Artificial Intelligence and Computer Science, Jiangnan University（人工智能与计算机科学系，江南大学）
FREAK: Frequency-modulated High-fidelity and Real-time Audio-driven Talking Portrait Synthesis
School of Computer Science and Engineering, Southeast University（计算机科学与工程学院，东南大学）；Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications, Ministry of Education, China（新一代人工智能技术及其交叉应用重点实验室，教育部，中国）
Peking University（北京大学）；Advanced Institute of Information Technology, Peking University（北京大学信息技术高等研究院）；Tencent America（腾讯美国）
Generating portrait images by controlling the motions of existing faces is an important task of great consequence to social media industries. For easy use and intuitive control, semantically meaningful and fully disentangled parameters should be used as modifications. However, many existing techniques do not provide such fine-grained controls or use indirect editing methods i.e. mimic motions of other individuals. In this paper, a Portrait Image Neural Renderer (PIRenderer) is proposed to control the face motions with the parameters of three-dimensional morphable face models (3DMMs). The propo…
Silence is Golden: Leveraging Adversarial Examples to Nullify Audio Control in LDM-based Talking-Head Generation
ReLER, CCAI, Zhejiang University（ReLER，中国人工智能学会，浙江大学）；School of Cyber Science and Technology, Sun Yat-sen University（信息科学与技术学院，孙中山大学）；Department of Statistics, University of Wisconsin–Madison（统计学系，威斯康星大学麦迪逊分校）
Unlock Pose Diversity: Accurate and Efficient Implicit Keypoint-based Spatiotemporal Diffusion for Audio-driven Talking Portrait
University of Liverpool（利物浦大学）；Ant Group（蚂蚁集团）；Xi’an Jiaotong-Liverpool University（西交利物浦大学）；Duke Kunshan University（昆山杜克大学）；Ricoh Software Research Center（理光软件研究中心）
GoHD: Gaze-oriented and Highly Disentangled Portrait Animation with Rhythmic Poses and Realistic Expression
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；NIO（蔚来）；Banma（斑马）；Beihang University（北京航空航天大学）
Mamba-Enhanced Implicit Motion Learning for Audio-Driven Portrait Animation
Fujian Provincial Natural Science Foundation of China（福建省自然科学基金委员会）；Giant Interactive Group Inc.（巨匠互动集团有限公司）；National Natural Science Foundation of China（国家自然科学基金委员会）
To the best of our knowledge, we first present a live system that generates personalized photorealistic talking-head animation only driven by audio signals at over 30 fps. Our system contains three stages. The first stage is a deep neural network that extracts deep audio features along with a manifold projection to project the features to the target person's speech space. In the second stage, we learn facial dynamics and motions from the projected audio features. The predicted motions include head poses and upper body motions, where the former is generated by an autoregressive probabilistic mo…
College of Artificial Intelligence and Automation, Hohai University（人工智能与自动化学院，河海大学）；College of DaYu, Hohai University（大禹学院，河海大学）；College of Water Conserwancy and Hydropower Engineering, Hohai University（水利水电工程学院，河海大学）；College of Computer Science and Technology, Zhejiang University of Technology（计算机科学与技术学院，浙江工业大学）
