Active shape models—their training and application
Computer Vision and Image Understanding
Intelligent scissors for image composition
SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
SIGGRAPH '96 Proceedings of the 23rd annual conference on Computer graphics and interactive techniques
Neural Network-Based Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Fast texture synthesis using tree-structured vector quantization
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Image quilting for texture synthesis and transfer
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
Face Detection in Color Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing
Rotation Invariant Neural Network-Based Face Detection
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
Texture Synthesis by Non-Parametric Sampling
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Fragment-based image completion
ACM SIGGRAPH 2003 Papers
ACM SIGGRAPH 2003 Papers
Robust Real-Time Face Detection
International Journal of Computer Vision
ACM SIGGRAPH 2004 Papers
Image completion with structure propagation
ACM SIGGRAPH 2005 Papers
Soft scissors: an interactive tool for realtime high quality matting
ACM SIGGRAPH 2007 papers
Image Analysis and Synthesis of Skin Color Textures by Wavelet Transform
SSIAI '06 Proceedings of the 2006 IEEE Southwest Symposium on Image Analysis and Interpretation
A Closed-Form Solution to Natural Image Matting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face swapping: automatically replacing faces in photographs
ACM SIGGRAPH 2008 papers
Example-based hair geometry synthesis
ACM SIGGRAPH 2009 papers
Visio-lization: generating novel facial images
ACM SIGGRAPH 2009 papers
A Run-Based Two-Scan Labeling Algorithm
IEEE Transactions on Image Processing
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The modern trend of diversification and personalization has encouraged people to boldly express their differentiation and uniqueness in many aspects, and one of the noticeable evidences is the wide variety of hairstyles that we could observe today. Given the needs for hairstyle customization, approaches or systems, ranging from 2D to 3D, or from automatic to manual, have been proposed or developed to digitally facilitate the choice of hairstyles. However, nearly all existing approaches suffer from providing realistic hairstyle synthesis results. By assuming the inputs to be 2D photos, the vividness of a hairstyle re-synthesis result relies heavily on the removal of the original hairstyle, because the co-existence of the original hairstyle and the newly re-synthesized hairstyle may lead to serious artifact on human perception. We resolve this issue by extending the active shape model to more precisely extract the entire facial contour, which can then be used to trim away the hair from the input photo. After hair removal, the facial skin of the revealed forehead needs to be recovered. Since the skin texture is non-stationary and there is little information left, the traditional texture synthesis and image inpainting approaches do not fit to solve this problem. Our proposed method yields a more desired facial skin patch by first interpolating a base skin patch, and followed by a non-stationary texture synthesis. In this paper, we also would like to reduce the user assistance during such a process as much as possible. We have devised a new and friendly facial contour and hairstyle adjusting mechanism that make it extremely easy to manipulate and fit a desired hairstyle onto a face. In addition, our system is also equipped with the functionality of extracting the hairstyle from a given photo, which makes our work more complete. Moreover, by extracting the face from the input photo, our system allows users to exchange faces as well. In the end of this paper, our re-synthesized results are shown, comparisons are made, and user studies are conducted as well to further demonstrate the usefulness of our system.