We propose a method to build in real-time animated 3D head models using a consumer-grade RGB-D camera. Our proposed method is the first one to provide simultaneously comprehensive facial motion tracking and a detailed 3D model of the user's head. Anyone's head can be instantly reconstructed and his facial motion captured without requiring any training or pre-scanning. The user starts facing the camera with a neutral expression in the first frame, but is free to move, talk and change his face expression as he wills otherwise. The facial motion is captured using a blendshape animation model while geometric details are captured using a Deviation image mapped over the template mesh. We contribute with an efficient algorithm to grow and refine the deforming 3D model of the head on-the-fly and in real-time. We demonstrate robust and high-fidelity simultaneous facial motion capture and 3D head modeling results on a wide range of subjects with various head poses and facial expressions.
Computer Vision Center, UAB（UAB计算机视觉中心）；University of Barcelona（巴塞罗那大学）；Robotics Institute, CMU（CMU机器人研究所）；University of Pittsburgh（匹兹堡大学）
Facial expressions are an important way through which humans interact socially. Building a system capable of automatically recognizing facial expressions from images and video has been an intense field of study in recent years. Interpreting such expressions remains challenging and much research is needed about the way they relate to human affect. This paper presents a general overview of automatic RGB, 3D, thermal and multimodal facial expression analysis. We define a new taxonomy for the field, encompassing all steps from face detection to facial expression recognition, and describe and class…
Facial landmark detection, head pose estimation, and facial deformation analysis are typical facial behavior analysis tasks in computer vision. The existing methods usually perform each task independently and sequentially, ignoring their interactions. To tackle this problem, we propose a unified framework for simultaneous facial landmark detection, head pose estimation, and facial deformation analysis, and the proposed model is robust to facial occlusion. Following a cascade procedure augmented with model-based head pose estimation, we iteratively update the facial landmark locations, facial o…
We describe a method for estimating human head pose in a color image that contains enough of information to locate the head silhouette and detect non-trivial color edges of individual facial features. The method works by spotting the human head on an arbitrary background, extracting the head outline, and locating facial features necessary to describe the head orientation in the 3D space. It is robust enough to work with both color and gray-level images featuring quasi-frontal views of a human head under variable lighting conditions.
Estimation of the Heart rate from the facial video has a number of applications in the medical and fitness industries. Additionally, it has become useful in the field of gaming as well. Several approaches have been proposed to seamlessly obtain the Heart rate from the facial video, but these approaches have had issues in dealing with motion and illumination artifacts. In this work, we propose a reliable HR estimation framework using the spectral reflectance of the user, which makes it robust to motion and illumination disturbances. We employ deep learning-based frameworks such as Faster RCNNs …
For realistic talking head generation, creating natural head motion while maintaining accurate lip synchronization is essential. To fulfill this challenging task, we propose DisCoHead, a novel method to disentangle and control head pose and facial expressions without supervision. DisCoHead uses a single geometric transformation as a bottleneck to isolate and extract head motion from a head-driving video. Either an affine or a thin-plate spline transformation can be used and both work well as geometric bottlenecks. We enhance the efficiency of DisCoHead by integrating a dense motion estimator a…
Pinscreen（Pinscreen公司）；University of Southern California（南加州大学）
We introduce the concept of unconstrained real-time 3D facial performance capture through explicit semantic segmentation in the RGB input. To ensure robustness, cutting edge supervised learning approaches rely on large training datasets of face images captured in the wild. While impressive tracking quality has been demonstrated for faces that are largely visible, any occlusion due to hair, accessories, or hand-to-face gestures would result in significant visual artifacts and loss of tracking accuracy. The modeling of occlusions has been mostly avoided due to its immense space of appearance var…
Seoul National University of Science and Technology（首尔科技大学）；Kumoh National Institute of Technology（金乌国立工科大学）
Facial expression in-the-wild is essential for various interactive computing domains. In this paper, we proposed an extended version of DAN model to address the VA estimation and facial expression challenges introduced in ABAW 2022. Our method produced preliminary results of 0.44 of mean CCC value for the VA estimation task, and 0.33 of the average F1 score for the expression classification task.
Facial expressions of emotion are a major channel in our daily communications, and it has been subject of intense research in recent years. To automatically infer facial expressions, convolutional neural network based approaches has become widely adopted due to their proven applicability to Facial Expression Recognition (FER) task.On the other hand Virtual Reality (VR) has gained popularity as an immersive multimedia platform, where FER can provide enriched media experiences. However, recognizing facial expression while wearing a head-mounted VR headset is a challenging task due to the upper h…
Samsung AI Center – Moscow（三星人工智能中心（莫斯科））；Skolkovo Institute of Science and Technology（斯科尔科沃科学技术研究院）
We present a system for real-time RGBD-based estimation of 3D human pose. We use parametric 3D deformable human mesh model (SMPL-X) as a representation and focus on the real-time estimation of parameters for the body pose, hands pose and facial expression from Kinect Azure RGB-D camera. We train estimators of body pose and facial expression parameters. Both estimators use previously published landmark extractors as input and custom annotated datasets for supervision, while hand pose is estimated directly by a previously published method. We combine the predictions of those estimators into a te…
University of Essex（埃塞克斯大学）；Technological Educational Institute of Crete（克里特技术教育学院）；Aalborg University（奥尔堡大学）
Despite significant recent advances in the field of head pose estimation and facial expression recognition, raising the cognitive level when analysing human activity presents serious challenges to current concepts. Motivated by the need of generating comprehensible visual representations from different sets of data, we introduce a system capable of monitoring human activity through head pose and facial expression changes, utilising an affordable 3D sensing technology (Microsoft Kinect sensor). An approach build on discriminative random regression forests was selected in order to rapidly and ac…
Center for Spoken Language Understanding, Oregon Health and Science University（俄勒冈健康与科学大学口语语言理解中心）
Automatic head frontal-view identification is challenging due to appearance variations caused by pose changes, especially without any training samples. In this paper, we present an unsupervised algorithm for identifying frontal view among multiple facial images under various yaw poses (derived from the same person). Our approach is based on Locally Linear Embedding (LLE), with the assumption that with yaw pose being the only variable, the facial images should lie in a smooth and low dimensional manifold. We horizontally flip the facial images and present two K-nearest neighbor protocols for th…
University of Exeter（埃克塞特大学）；Imperial College London（帝国理工学院）；Foundation for Research and Technology - Hellas (FORTH)（希腊研究与技术基金会）
In this paper, we propose a novel machine learning architecture for facial reenactment. In particular, contrary to the model-based approaches or recent frame-based methods that use Deep Convolutional Neural Networks (DCNNs) to generate individual frames, we propose a novel method that (a) exploits the special structure of facial motion (paying particular attention to mouth motion) and (b) enforces temporal consistency. We demonstrate that the proposed method can transfer facial expressions, pose and gaze of a source actor to a target video in a photo-realistic fashion more accurately than stat…
University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；NeuralX, Inc.（NeuralX公司）
The transfer of facial expressions from people to 3D face models is a classic computer graphics problem. In this paper, we present a novel, learning-based approach to transferring facial expressions and head movements from images and videos to a biomechanical model of the face-head-neck complex. Leveraging the Facial Action Coding System (FACS) as an intermediate representation of the expression space, we train a deep neural network to take in FACS Action Units (AUs) and output suitable facial muscle and jaw activation signals for the musculoskeletal model. Through biomechanical simulation, th…
FSRT: Facial Scene Representation Transformer for Face Reenactment from Factorized Appearance, Head-pose, and Facial Expression Features
Autonomous Intelligent Systems - Computer Science Institute VI and Center for Robotics, University of Bonn（波恩大学自主智能系统 - 计算机科学第六研究所与机器人中心）；Lamarr Institute for Machine Learning and Artificial Intelligence（拉马尔机器学习和人工智能研究所）
There have been tremendous improvements for facial landmark detection on general "in-the-wild" images. However, it is still challenging to detect the facial landmarks on images with severe occlusion and images with large head poses (e.g. profile face). In fact, the existing algorithms usually can only handle one of them. In this work, we propose a unified robust cascade regression framework that can handle both images with severe occlusion and images with large head poses. Specifically, the method iteratively predicts the landmark occlusions and the landmark locations. For occlusion estimation…
Renmin University of China（中国人民大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；Psyche AI Inc.（Psyche AI公司）；Chinese Academy of Sciences（中国科学院）；Tsinghua University（清华大学）
Achieving high synchronization in the synthesis of realistic, speech-driven talking head videos presents a significant challenge. Traditional Generative Adversarial Networks (GAN) struggle to maintain consistent facial identity, while Neural Radiance Fields (NeRF) methods, although they can address this issue, often produce mismatched lip movements, inadequate facial expressions, and unstable head poses. A lifelike talking head requires synchronized coordination of subject identity, lip movements, facial expressions, and head poses. The absence of these synchronizations is a fundamental flaw, …
Centre for Visual Information Technology, KCIS, IIIT Hyderabad（国际信息技术研究所（海得拉巴）KCIS 视觉信息技术中心）
Monocular head pose estimation requires learning a model that computes the intrinsic Euler angles for pose (yaw, pitch, roll) from an input image of human face. Annotating ground truth head pose angles for images in the wild is difficult and requires ad-hoc fitting procedures (which provides only coarse and approximate annotations). This highlights the need for approaches which can train on data captured in controlled environment and generalize on the images in the wild (with varying appearance and illumination of the face). Most present day deep learning approaches which learn a regression fu…
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…
This work presents a framework for tracking head movements and capturing the movements of the mouth and both the eyebrows in real-time. We present a head tracker which is a combination of a optical flow and a template based tracker. The estimation of the optical flow head tracker is used as starting point for the template tracker which fine-tunes the head estimation. This approach together with re-updating the optical flow points prevents the head tracker from drifting. This combination together with our switching scheme, makes our tracker very robust against fast movement and motion-blur. We …
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）
Toward Fine-Grained Facial Control in 3D Talking Head Generation
School of Cyber Science and Engineering, Southeast University（东南大学计算机科学与工程学院）；Lee Kong Chian School of Medicine, Nanyang Technological University（南洋理工大学李科金医学院）；School of Remote Sensing and Information Engineering, Wuhan University（武汉大学遥感与信息工程学院）；Purple Mountain Laboratories, Nanjing（南京紫金山实验室）；Engineering Research Center of Blockchain Application, Supervision And Management (Southeast University), Ministry of Education（教育部区块链应用、监督与管理工程研究中心（东南大学））；Department of Computer and Information Science, University of Macau（澳门大学计算机与信息科学系）；Department of Computer Science and Engineering, The Hong Kong University of Science and Technology（香港科技大学计算机科学与工程系）
Imperial College London（帝国理工学院）；University of Exeter（埃克塞特大学）
Over the past years, a substantial amount of work has been done on the problem of facial reenactment, with the solutions coming mainly from the graphics community. Head reenactment is an even more challenging task, which aims at transferring not only the facial expression, but also the entire head pose from a source person to a target. Current approaches either train person-specific systems, or use facial landmarks to model human heads, a representation that might transfer unwanted identity attributes from the source to the target. We propose head2headFS, a novel easily adaptable pipeline for …
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…
University of Erlangen-Nuremberg（埃尔朗根-纽伦堡大学）；Max-Planck-Institute for Informatics（马克斯·普朗克信息学研究所）；Stanford University（斯坦福大学）
We present Face2Face, a novel approach for real-time facial reenactment of a monocular target video sequence (e.g., Youtube video). The source sequence is also a monocular video stream, captured live with a commodity webcam. Our goal is to animate the facial expressions of the target video by a source actor and re-render the manipulated output video in a photo-realistic fashion. To this end, we first address the under-constrained problem of facial identity recovery from monocular video by non-rigid model-based bundling. At run time, we track facial expressions of both source and target video u…
In recent years, the main problem in e-learning has shifted from analyzing content to personalization of learning environment by Intelligence Tutoring Systems (ITSs). Therefore, by designing personalized teaching models, learners are able to have a successful and satisfying experience in achieving their learning goals. Affective Tutoring Systems (ATSs) are some kinds of ITS that can recognize and respond to affective states of learner. In this study, we designed, implemented, and evaluated a system to personalize the learning environment based on the facial emotions recognition, head pose esti…
