Synthesizing realistic facial expressions from photographs
Proceedings of the 25th annual conference on Computer graphics and interactive techniques
A Unified Theory of Uncalibrated Stereo for Both Perspective and Affine Cameras
Journal of Mathematical Imaging and Vision
Geometry of Multiple Affine Views
SMILE'98 Proceedings of the European Workshop on 3D Structure from Multiple Images of Large-Scale Environments
A New Linear Method for Euclidean Motion/Structure from Three Calibrated Affine Views
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Camera auto-calibration using a sequence of 2D images with small rotations
Pattern Recognition Letters
Synthesizing realistic facial expressions from photographs
ACM SIGGRAPH 2006 Courses
Synthesizing realistic facial expressions from photographs
SIGGRAPH '05 ACM SIGGRAPH 2005 Courses
View synthesis using stereo vision
View synthesis using stereo vision
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Use of uncalibrated images has found many applications such as image synthesis. However, it is not easy to specify the desired position of the new image in projective or af.ne space. This paper proposes to recover Euclidean structure from uncalibrated images using domain knowledge such as distances and angles. The knowledge we have is usually about an object category, but not very precise for the particular object being considered. The variation (fuzziness) is modeled as a Gaussian variable. Six types of common knowledge are formulated. Once we have a Euclidean description, the task to specify the desired position in Euclidean space becomes trivial. The proposed technique is then applied to synthesis of new facial images. A number of dif.culties existing in image synthesis are identified and solved. For example, we propose to use edge points to deal with occlusion.