3D Face Modeling from Perspective-Views and Contour-Based Generic-Model

Lee, Kam-sum; Wong, K.-Y.K.; Or, Siu-Hang; Fung, Yiu-fai

doi:10.1006/rtim.2000.0241

Cited by 9 publications

(7 citation statements)

References 16 publications

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“…It should be noted that the proposed system does not require any camera calibration, which is in contrast to [13,14]. In addition, unlike [3,4,5], the frontal and profile views do not need to be strictly orthogonal. Note that the input images for this system could be obtained using an ordinary digital camera, which makes the system easy to use and inexpensive.…”

Section: Overview Of the Proposed Systemmentioning

confidence: 85%

“…However, it is very expensive and difficult to operate to the extent that the common end-users cannot use it. On the other hand, several studies have been devoted to the creation of face models from two or three views [3,4,5]. The views need to be strictly orthogonal, which is difficult to achieve when using a common handheld camera without special mounter setup.…”

Section: Previous Workmentioning

confidence: 99%

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Image-Based 3D Face Modeling System

Park

Zhang

Vezhnevets

2005

EURASIP J. Adv. Signal Process.

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This paper describes an automatic system for 3D face modeling using frontal and profile images taken by an ordinary digital camera. The system consists of four subsystems including frontal feature detection, profile feature detection, shape deformation, and texture generation modules. The frontal and profile feature detection modules automatically extract the facial parts such as the eye, nose, mouth, and ear. The shape deformation module utilizes the detected features to deform the generic head mesh model such that the deformed model coincides with the detected features. A texture is created by combining the facial textures augmented from the input images and the synthesized texture and mapped onto the deformed generic head model. This paper provides a practical system for 3D face modeling, which is highly automated by aggregating, customizing, and optimizing a bunch of individual computer vision algorithms. The experimental results show a highly automated process of modeling, which is sufficiently robust to various imaging conditions. The whole model creation including all the optional manual corrections takes only 2 3 minutes.

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Section: Overview Of the Proposed Systemmentioning

confidence: 85%

Section: Previous Workmentioning

confidence: 99%

Image-Based 3D Face Modeling System

Park

Zhang

Vezhnevets

2005

EURASIP J. Adv. Signal Process.

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“…• In [93], contour features were seen to provide a more compact representation over mesh features, achieving a real-time performance in calculating deformation parameters for a 3D face modeling application.…”

Section: Determining and Using Appropriate Featuresmentioning

confidence: 99%

Real-Time Image and Video Processing: From Research to Reality

Kehtarnavaz¹,

Gamadia²

2006

Synthesis Lectures on Image, Video, and Multimedia Processing

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This book presents an overview of the guidelines and strategies for transitioning an image or video processing algorithm from a research environment into a real-time constrained environment. Such guidelines and strategies are scattered in the literature of various disciplines including image processing, computer engineering, and software engineering, and thus have not previously appeared in one place. By bringing these strategies into one place, the book is intended to serve the greater community of researchers, practicing engineers, industrial professionals, who are interested in taking an image or video processing algorithm from a research environment to an actual real-time implementation on a resource constrained hardware platform. These strategies consist of algorithm simplifications, hardware architectures, and software methods. Throughout the book, carefully selected representative examples from the literature are presented to illustrate the discussed concepts. After reading the book, the readers are exposed to a wide variety of techniques and tools, which they can then employ for designing a real-time image or video processing system of interest. KEYWORDSReal-time image and video processing, Real-time implementation strategies, Algorithmic simplifications for real-time image and video processing, Hardware platforms for real-time image and video processing, Software methods for real-time image and video processing v

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“…Our method can be considered as an extension of Lowe's method. In applications that require the tracking of the pose of an unknown object, e.g., the pose of a person's head [10], our algorithm could be used to recover the pose even if a model of the person's head is not available.…”

Section: Introductionmentioning

confidence: 99%

Model reconstruction and pose acquisition using extended Lowe's method

Chang

Wong

2005

IEEE Trans. Multimedia

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Finding the pose and structure of an unknown object from an image sequence has many applications in graphics, virtual reality, and multimedia processing. In this paper, we address this problem by using a two-stage iterative method. Starting from an initial guess of the structure, the first stage estimates the pose of the object. The second stage uses the estimated pose information to refine the structure. This process is repeated until the difference between the observed data and data re-projected from the estimated model is minimized. This method is a variation of the classical bundle adjustment method, but is faster in execution and is simpler to implement. We used the Kanade-Lucas-Tomasi feature tracker for obtaining the image features. Synthetic and real data have been tested with good results.Index Terms-3-D structure acquisition, bundle adjustment, Lowe's method, pose estimation, structure from motion.

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3D Face Modeling from Perspective-Views and Contour-Based Generic-Model

Cited by 9 publications

References 16 publications

Image-Based 3D Face Modeling System

Image-Based 3D Face Modeling System

Real-Time Image and Video Processing: From Research to Reality

Model reconstruction and pose acquisition using extended Lowe's method

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