Radial basis functions for multivariable interpolation: a review
Algorithms for approximation
A morphable model for the synthesis of 3D faces
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
Implicit fairing of irregular meshes using diffusion and curvature flow
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
Generation of a 3-D Face Model from One Camera
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Efficient, Robust and Accurate Fitting of a 3D Morphable Model
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Visual Modeling with a Hand-Held Camera
International Journal of Computer Vision
Stable Real-Time 3D Tracking Using Online and Offline Information
IEEE Transactions on Pattern Analysis and Machine Intelligence
Implicit Meshes for Surface Reconstruction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stereo Using Monocular Cues within the Tensor Voting Framework
IEEE Transactions on Pattern Analysis and Machine Intelligence
An investigation of model bias in 3d face tracking
AMFG'05 Proceedings of the Second international conference on Analysis and Modelling of Faces and Gestures
Feature detection of triangular meshes based on tensor voting theory
Computer-Aided Design
A new framework for 3D face reconstruction for self-occluded images
International Journal of Computational Vision and Robotics
| Hi-index | 0.00 |
This paper describes a model-assisted system for reconstruction of 3D faces from a single consumer quality camera using a structure from motion approach. Typical multi-view stereo approaches use the motion of a sparse set of features to compute camera pose followed by a dense matching step to compute the final object structure. Accurate pose estimation depends upon precise identification and matching of feature points between images, but due to lack of texture on large areas of the face, matching is prone to errors. To deal with outliers in both the sparse and dense matching stages, previous work either relies on a strong prior model for face geometry or imposes restrictions on the camera motion. Strong prior models result in a serious compromise in final reconstruction quality and typically bear a signature resemblance to a generic or mean face. Model-based techniques, while giving the appearance of face detail, in fact carry this detail over from the model prior. Face features such as beards, moles, and other characteristic geometry are lost. Motion restrictions such as allowing only pure rotation are nearly impossible to satisfy by the end user, especially with a handheld camera. We significantly improve the robustness and flexibility of existing monocular face reconstruction techniques by introducing a deformable generic face model only at the pose estimation, face segmentation, and preprocessing stages. To preserve data fidelity in the final reconstruction, this generic model is discarded completely and dense matching outliers are removed using tensor voting: a purely data-driven technique. Results are shown from a complete end to end system.