Model Based Face Reconstruction for Animation

International Journal of Computer Vision, 2004

Image and Vision Computing, 2000

This paper describes an efficient method to make individual faces for animation from several possible inputs. We present a method to reconstruct 3D facial model for animation from two orthogonal pictures taken from front and side views or from range data obtained from any available resources. It is based on extracting features on a face in a semiautomatic way and modifying a generic model with detected feature points. Then the fine modifications follow if range data is available. Automatic texture mapping is employed using a composed image from the two images. The reconstructed 3Dface can be animated immediately with given expression parameters. Several faces by one methodology applied to different input data to get a final animatable face are illustrated.

The Journal of …, 2001

Generating realistic 3D human face models and facial animations has been a persistent challenge in computer graphics. We have developed a system that constructs textured 3D face models from videos with minimal user interaction. Our system takes images and video sequences of a face with an ordinary video camera. After five manual clicks on two images to tell the system where the eye corners, nose top and mouth corners are, the system automatically generates a realistic looking 3D human head model and the constructed model can be animated immediately. A user, with a PC and an ordinary camera, can use our system to generate his/her face model in a few minutes.

2002

This thesis proposes a model-based 3-D talking head animation system and then constructs a simple 3-D face model and its animation by using Virtual Reality Model­ ing Language (VRML) 2.0 in conjunction with a VRML's Application Programming Interface (API), JAVA. The system extracts facial feature information from a digital video source. The face detection and facial feature extraction are prerequisite stages to track the key facial features throughout the video sequence. Face detection is done by using relevant facial information contained in the normalized YCbCr color space. Independent Component Analysis (ICA) approach is applied to the localized facial images to identify major facial components of a face. Then, an image processing approach is deployed to extract and track the key facial features precisely. Streams of the extracted and determined facial feature parameters are transferred to the ani­ mation control points of the designed VRML 3-D facial model. Since the face mo...

2005

The creation of personalised 3D characters has evolved to provide a high degree of realism in both appearance and animation. Further to the creation of generic characters the capabilities exist to create a personalised character from images of an individual. This provides the possibility of immersing an individual into a virtual world. Feature detection, particularly on the face, can be used to greatly enhance the realism of the model. To address this innovative contour based templates are used to extract an individual from four orthogonal views providing localisation of the face. Then adaptive facial feature extraction from multiple views is used to enhance the realism of the model.

2010 23rd SIBGRAPI Conference on Graphics, Patterns and Images, 2010

In this paper we introduce a Facial Animation system using real three-dimensional models of people, acquired by a 3D scanner. We consider a dataset composed by models displaying different facial expressions and a linear interpolation technique is used to produce a smooth transition between them. One-to-one correspondences between the meshes of each facial expression are required in order to apply the interpolation process. Instead of focusing in the computation of dense correspondence, some points are selected and a triangulation is defined, being refined by consecutive subdivisions, that compute the matchings of intermediate points. We are able to animate any model of the dataset, given its texture information for the neutral face and the geometry information for all the expressions along with the neutral face. This is made by computing matrices with the variations of every vertex when changing from the neutral face to the other expressions. The knowledge of the matrices obtained in this process makes it possible to animate other models given only the texture and geometry information of the neutral face. Furthermore, the system uses 3D reconstructed models, being capable of generating a three-dimensional facial animation from a single 2D image of a person. Also, as an extension of the system, we use artificial models that contain expressions of visemes, that are not part of the expressions of the dataset, and their displacements are applied to the real models. This allows these models to be given as input to a speech synthesis application in which the face is able to speak phrases typed by the user. Finally, we generate an average face and increase the displacements between a subject from the dataset and the average face, creating, automatically, a caricature of the subject.

2015

Facial animation done by performance capture techniques is a subject of growing importance while the quality of graphics and animation in entertainment industry is constantly improving. Most realistic results in facial animation can be obtained using realistic acquisition of human face by various three-dimensional scanning devices. Such registration, however, is prone to errors related to specifity of scanned area, therefore some preprocessing is needed so the obtained model could be used. The aim of this paper is to present typical, face-specific issues as well as solutions related to preprocessing of mesh constructed by scanning techniques. Mesh traversing is applied to reduce number of noisy data related to light dispersion and reflection. Non-manifold edges and vertices are corrected on basis of specifity of studied area, strips of noisy triangles typical for hair are removed and holes typical for chin-neck part of model are filled. Resulting mesh represents single, continuous surface without non-manifold edges or vertices and with hair-related noise removed. Although model after preprocessing is ready for animation, future study to minimize data not related to facial expressions might be needed.

Iraqi Journal for Electrical and Electronic Engineering, 2021

Animating human face presents interesting challenges because of its familiarity as the face is the part utilized to recognize individuals. This paper reviewed the approaches used in facial modeling and animation and described their strengths and weaknesses. Realistic face animation of computer graphic models of human faces can be hard to achieve as a result of the many details that should be approximated in producing realistic facial expressions. Many methods have been researched to create more and more accurate animations that can efficiently represent human faces. We described the techniques that have been utilized to produce realistic facial animation. In this survey, we roughly categorized the facial modeling and animation approach into the following classes: blendshape or shape interpolation, parameterizations, facial action coding system-based approaches, moving pictures experts group-4 facial animation, physics-based muscle modeling, performance driven facial animation, visua...

Signal Processing: Image Communication, 2002

There are two main processes to create a 3D animatable facial model from photographs. The first is to extract features such as eyes, nose, mouth, and chin curves on the photographs. The second is to create 3D individualized facial model using extracted feature information. The final facial model is expected to have an individualized shape, photograph-realistic skin color, and animatable structures. Here, we describe our novel approach to detect features automatically using a statistical analysis for facial information. We are not only interested in the location of the features but also the shape of local features. How to create 3D models from detected features is also explained and several resulting 3D facial models are illustrated and discussed.

Signal Processing: Image Communication, 2006

In this paper, we present an image-based method for the tracking and rendering of faces. We use the algorithm in an immersive video conferencing system where multiple participants are placed in a common virtual room. This requires viewpoint modification of dynamic objects. Since hair and uncovered areas are difficult to model by pure 3-D geometry-based warping, we add image-based rendering techniques to the system. By interpolating novel views from a 3-D image volume, natural looking results can be achieved. The image-based component is embedded into a geometry-based approach in order to limit the number of images that have to be stored initially for interpolation. Also temporally changing facial features are warped using the approximate geometry information. Both geometry and image cube data are jointly exploited in facial expression analysis and synthesis.