We present new techniques for creating photorealistic textured 3D facial models from photographs of a human subject, and for creating smooth transitions between different facial expressions by morphing between these different models. Starting from several uncalibrated views of a human subject, we employ a user-assisted technique to recover the camera poses corresponding to the views as well as the 3D coordinates of a sparse set of chosen locations on the subject's face. A scattered data interpolation technique is then used to deform a generic face mesh to fit the particular geometry of the subject's face. Having recovered the camera poses and the facial geometry, we extract from the input images one or more texture maps for the model. This process is repeated for several facial expressions of a particular subject. To generate transitions between these facial expressions we use 3D shape morphing between the corresponding face models, while at the same time blending the corresponding tex...

In this paper a system is presented which automatically registers and stitches textures acquired from multiple photographic images onto the surface of a given corresponding 3D model. Within this process the camera position, direction and field of view must be determined for each of the images. For this registration, which aligns a 2D image to a 3D model we present an efficient hardware-accelerated silhouette-based algorithm working on different image resolutions that accurately registers each image without any user interaction. Besides the silhouettes, also the given texture information can be used to improve accuracy by comparing one stitched texture to already registered images resulting in a global multi-view optimization. After the 3D-2D registration for each part of the 3D model's surface the view is determined which provides the best available texture. Textures are blended at the borders of regions assigned to different views.

this paper a system is presented that automatically registers and stitches textures acquired from multiple photographic images onto the surface of a given corresponding 3D model. Within this process the camera position, direction, and field of view must be determined for each of the images. For this registration, which aligns a 2D image to a 3D model, we present an efficient hardware-accelerated silhouette-based algorithm working on different image resolutions that accurately registers each image without any user interaction. Besides the silhouettes, the given texture information also can be used to improve accuracy by comparing one stitched texture to already registered images resulting in a global multiview optimization. After the 3D--2D registration for each part of the 3D model's surface the view is determined which provides the best available texture. Textures are blended at the borders of regions assigned to different views. c Key Words: texture stitching; 3D--2D registration; object representation

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. Keywords: Face modeling, facial animation, geometric modeling, computer vision 1 Introduction One of the most interesting and difficult problems in computer graphics is the effortless generation of realistic looking, animated human face models. Animated face models are essential to computer games, film making, online chat, ...

The discriminating power of three dimensional (3D) descriptors extracted from 3D human face surfaces is analyzed. An automatic face recognition system using different subsets of the descriptor set has been implemented and tested. We used 420 3D-facial meshes belonging to 60 individuals, including views presenting light rotations and facial expressions, for the experiments. An HK segmentation (based in the signs of the mean and Gaussian curvatures) for isolating regions of pronounced curvature has been performed. Eighty six descriptors have been obtained from the segmented regions. The thirty five more discriminating ones in frontal views provide 78 % of recognition success rate when best match is selected, and a 92 % of recognition success is obtained when the five best matches are selected.

We present an easy, practical and efficient full body cloning methodology. This system utilizes photos taken from the front, side and back of a person in any given imaging environment without requiring a special background or a controlled illuminating condition. A seamless generic body specified in the VRML H-Anim 1.1 format is used to generate an individualized virtual human. The system is composed of two major components: face-cloning and body-cloning. The face-cloning component uses feature points on front and side images and then applies DFFD for shape modification. Next a fully automatic seamless texture mapping is generated for 360 o coloring on a 3D polygonal model. The body-cloning component has two steps: (i) feature points specification, which enables automatic silhouette detection in an arbitrary background (ii) two-stage body modification by using feature points and body silhouette respectively. The final integrated human model has photo-realistic animatable face,...

We present a new set of techniques for modeling and animating realistic faces from photographs and videos. Given a set of face photographs taken simultaneously, our modeling technique allows the interactive recovery of a textured 3D face model. By repeating this process for several facial expressions, we acquire a set of faces models that can be linearly combined to express a wide range of expressions. Given a video sequence, this linear face model can be used to estimate the face position, orientation, and facial expression at each frame. We illustrate these techniques on several datasets and demonstrate robust estimations of detailed face geometry and motion. 1

. This paper describes a combined method of facial reconstruction and morphing between two heads, showing the extensive usage of feature points detected from pictures. We first present an efficient method to generate a 3D head for animation from picture data and then a simple method to do 3Dshape interpolation and 2D morphing based on triangulation. The basic idea is to generate an individualized head modified from a generic model using orthogonal picture input, then process automatic texture mapping with texture image generation by combining orthogonal pictures and coordinate generation by projection from a resulted head in front, right and left views, which results a nice triangulation on texture image. Then an intermediate shape can be obtained from interpolation between two different persons. The morphing between 2D images is processed by generating an intermediate image and new texture coordinate. Texture coordinates are interpolated linearly, and the texture image is...

We have developed an easy-to-use and cost-effective system to construct textured 3D animated face models from videos with minimal user interaction. This is a particularly challenging task for faces due to a lack of prominent textures. We develop a robust system by following a model-based approach: we make full use of generic knowledge of faces in head motion determination, head tracking, model fitting, and multiple-view bundle adjustment. Our system first takes, with an ordinary video camera, images of a face of a person sitting in front of the camera turning their head from one side to the other. After five manual clicks on two images to indicate the position of the eye corners, nose tip and mouth corners, the system automatically generates a realistic looking 3D human head model that can be animated immediately (different poses, facial expressions and talking). A user, with a PC and a video camera, can use our system to generate his/her face model in a few minutes. The face model can then be imported in his/her favorite game, and the user sees themselves and their friends take part in the game they are playing. We have demonstrated the system on a laptop computer live at many events, and constructed face models for hundreds of people. It works robustly under

This paper presents a novel approach to aid face recognition: Using multiple views of a face, we construct a 3D model instead of directly using the 2D images for recognition. Our framework is designed for videos, which contain many instances of a target face from a sequence of slightly differing views, as opposed to a single static picture of the face. Specifically, we reconstruct the 3D face shapes from two orthogonal views and select features based on pairwise distances between landmark points on the model using Fisher's Linear Discriminant. While 3D face shape reconstruction is sensitive to the quality of the feature point localization, our experiments show that 3D reconstruction together with the regularized Fisher's Linear Discriminant can provide highly accurate face recognition from multiple facial views. Experiments on the Carnegie Mellon PIE (Pose, Illumination and Expressions) database containing 68 people’s faces with at least 3 expressions under varying lighting conditions demonstrate vastly improved performance 1.