In this paper, we propose a new technique to generate virtual views of three-dimensional (3D) models. The technique is implemented into our facial pose transformation system, which takes only one frontal image and transforms it into virtual views. In our system, to overcome the complex of 3D geometric model, Image Based Rendering based algorithm and mesh-based methods are applied. We also introduce our new Invertible Meshwarp Algorithm, which is developed based on Two-pass Meshwarp Algorithm. Firstly, in the system, for any given person, we take a frontal face image to compose a frontal mesh for it. The standard mesh set of a specific person is created for several face sides; front, half left, half right, left and right side. The other meshes are then automatically generated based on the standard mesh set and the frontal mesh. Continually, we use Invertible Meshwarp Algorithm, which improvably solves the overlap or inversion of neighbor vertices of those created meshes. This step will finalize the generation of different views or the virtual looks of the frontal face image. We then evaluate our transformation system performance by comparing the normalized distance between several feature points in the real and transformed face images. The system is built based on C/C++ language and our result shows that the average error in the feature location is about 7% of the distance from the center of both eyes to the center of a mouth between the actual and transformed face images.
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The Hung PHAN, Byung Hwan JUN, "Virtual View Generation from a Frontal Face Image Using Invertible Meshwarp Algorithm" in IEICE TRANSACTIONS on Fundamentals,
vol. E87-A, no. 6, pp. 1401-1408, June 2004, doi: .
Abstract: In this paper, we propose a new technique to generate virtual views of three-dimensional (3D) models. The technique is implemented into our facial pose transformation system, which takes only one frontal image and transforms it into virtual views. In our system, to overcome the complex of 3D geometric model, Image Based Rendering based algorithm and mesh-based methods are applied. We also introduce our new Invertible Meshwarp Algorithm, which is developed based on Two-pass Meshwarp Algorithm. Firstly, in the system, for any given person, we take a frontal face image to compose a frontal mesh for it. The standard mesh set of a specific person is created for several face sides; front, half left, half right, left and right side. The other meshes are then automatically generated based on the standard mesh set and the frontal mesh. Continually, we use Invertible Meshwarp Algorithm, which improvably solves the overlap or inversion of neighbor vertices of those created meshes. This step will finalize the generation of different views or the virtual looks of the frontal face image. We then evaluate our transformation system performance by comparing the normalized distance between several feature points in the real and transformed face images. The system is built based on C/C++ language and our result shows that the average error in the feature location is about 7% of the distance from the center of both eyes to the center of a mouth between the actual and transformed face images.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e87-a_6_1401/_p
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@ARTICLE{e87-a_6_1401,
author={The Hung PHAN, Byung Hwan JUN, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Virtual View Generation from a Frontal Face Image Using Invertible Meshwarp Algorithm},
year={2004},
volume={E87-A},
number={6},
pages={1401-1408},
abstract={In this paper, we propose a new technique to generate virtual views of three-dimensional (3D) models. The technique is implemented into our facial pose transformation system, which takes only one frontal image and transforms it into virtual views. In our system, to overcome the complex of 3D geometric model, Image Based Rendering based algorithm and mesh-based methods are applied. We also introduce our new Invertible Meshwarp Algorithm, which is developed based on Two-pass Meshwarp Algorithm. Firstly, in the system, for any given person, we take a frontal face image to compose a frontal mesh for it. The standard mesh set of a specific person is created for several face sides; front, half left, half right, left and right side. The other meshes are then automatically generated based on the standard mesh set and the frontal mesh. Continually, we use Invertible Meshwarp Algorithm, which improvably solves the overlap or inversion of neighbor vertices of those created meshes. This step will finalize the generation of different views or the virtual looks of the frontal face image. We then evaluate our transformation system performance by comparing the normalized distance between several feature points in the real and transformed face images. The system is built based on C/C++ language and our result shows that the average error in the feature location is about 7% of the distance from the center of both eyes to the center of a mouth between the actual and transformed face images.},
keywords={},
doi={},
ISSN={},
month={June},}
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TY - JOUR
TI - Virtual View Generation from a Frontal Face Image Using Invertible Meshwarp Algorithm
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1401
EP - 1408
AU - The Hung PHAN
AU - Byung Hwan JUN
PY - 2004
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E87-A
IS - 6
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - June 2004
AB - In this paper, we propose a new technique to generate virtual views of three-dimensional (3D) models. The technique is implemented into our facial pose transformation system, which takes only one frontal image and transforms it into virtual views. In our system, to overcome the complex of 3D geometric model, Image Based Rendering based algorithm and mesh-based methods are applied. We also introduce our new Invertible Meshwarp Algorithm, which is developed based on Two-pass Meshwarp Algorithm. Firstly, in the system, for any given person, we take a frontal face image to compose a frontal mesh for it. The standard mesh set of a specific person is created for several face sides; front, half left, half right, left and right side. The other meshes are then automatically generated based on the standard mesh set and the frontal mesh. Continually, we use Invertible Meshwarp Algorithm, which improvably solves the overlap or inversion of neighbor vertices of those created meshes. This step will finalize the generation of different views or the virtual looks of the frontal face image. We then evaluate our transformation system performance by comparing the normalized distance between several feature points in the real and transformed face images. The system is built based on C/C++ language and our result shows that the average error in the feature location is about 7% of the distance from the center of both eyes to the center of a mouth between the actual and transformed face images.
ER -