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Journal ArticleDOI

Robust and Rapid Generation of Animated Faces from Video Images: A Model-Based Modeling Approach

TLDR
An easy-to-use and cost-effective system to construct textured 3D animated face models from videos with minimal user interaction, which makes full use of generic knowledge of faces in head motion determination, head tracking, model fitting, and multiple-view bundle adjustment.
Abstract
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 various environment settings.

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Citations
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Journal ArticleDOI

A quasi-dense approach to surface reconstruction from uncalibrated images

TL;DR: A complete automatic and practical system of 3D modeling from raw images captured by hand-held cameras to surface representation is proposed, demonstrating the superior performance of the quasi-dense approach with respect to the standard sparse approach in robustness, accuracy, and applicability.
Proceedings ArticleDOI

Dense 3D face alignment from 2D videos in real-time

TL;DR: A 3D cascade regression approach in which facial landmarks remain invariant across pose over a range of approximately 60 degrees is developed, which strongly support the validity of real-time, 3D registration and reconstruction from 2D video.
Book ChapterDOI

3D deformable face tracking with a commodity depth camera

TL;DR: In this article, a regularized maximum likelihood deformable model fitting (DMF) algorithm is developed, with special emphasis on handling the noisy input depth data, which allows more elaborate modeling of the sensor's accuracy.
Book ChapterDOI

A Generative Shape Regularization Model for Robust Face Alignment

TL;DR: This paper presents a robust face alignment system that is capable of dealing with exaggerating expressions, large occlusions, and a wide variety of image noises and can effectively recover sufficient shape details from very noisy observations.
References
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Journal ArticleDOI

Snakes : Active Contour Models

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Proceedings ArticleDOI

A Combined Corner and Edge Detector

TL;DR: The problem the authors are addressing in Alvey Project MMI149 is that of using computer vision to understand the unconstrained 3D world, in which the viewed scenes will in general contain too wide a diversity of objects for topdown recognition techniques to work.
Journal ArticleDOI

A flexible new technique for camera calibration

TL;DR: A flexible technique to easily calibrate a camera that only requires the camera to observe a planar pattern shown at a few (at least two) different orientations is proposed and advances 3D computer vision one more step from laboratory environments to real world use.
Journal ArticleDOI

Determining optical flow

TL;DR: In this paper, a method for finding the optical flow pattern is presented which assumes that the apparent velocity of the brightness pattern varies smoothly almost everywhere in the image, and an iterative implementation is shown which successfully computes the Optical Flow for a number of synthetic image sequences.
Proceedings ArticleDOI

Determining Optical Flow

TL;DR: In this article, a method for finding the optical flow pattern is presented which assumes that the apparent velocity of the brightness pattern varies smoothly almost everywhere in the image, and an iterative implementation is shown which successfully computes the Optical Flow for a number of synthetic image sequences.
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