Visio-lization: Generating Novel Facial Images.
ACM TRANSACTIONS ON GRAPHICS.
(pp. ? - ?).
ASSOC COMPUTING MACHINERY
Our goal is to generate novel realistic images of faces using a model trained from real examples. This model consists of two components: First we consider face images as samples from a texture with spatially varying statistics and describe this texture with a local non-parametric model. Second, we learn a parametric global model of all of the pixel values. To generate realistic faces, we combine the strengths of both approaches and condition the local non-parametric model on the global parametric model. We demonstrate that with appropriate choice of local and global models it is possible to reliably generate new realistic face images that do not correspond to any individual in the training data. We extend the model to cope with considerable intra-class variation ( pose and illumination). Finally, we apply our model to editing real facial images: we demonstrate image in-painting, interactive techniques for improving synthesized images and modifying facial expressions.
|Title:||Visio-lization: Generating Novel Facial Images|
|Event:||ACM SIGGRAPH Conference 2009|
|Location:||New Orleans, LA|
|Keywords:||face, texture synthesis, non-parametric sampling, MODELS, FACES|
|UCL classification:||UCL > School of BEAMS > Faculty of Engineering Science > Computer Science|
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