Towards automatic image editing: Learning to see another you
Towards automatic image editing: Learning to see another you
Ghodrati, Amir and Jia, Xu and Pedersoli, Marco and Tuytelaars, Tinne
British Machine Vision Conference 2016, BMVC 2016 2016
Abstract : In this paper we propose a method that aims at automatically editing an image by altering its attributes. More specifically, given an image of a certain class (e.g. a human face), the method should generate a new image as similar as possible to the given one, but with an altered visual attribute (e.g. the same face with a new pose or a different illumination). To this end, we propose a solution following an encoder-decoder pipeline. The desired attribute and the input image are independently encoded into a convolutional network and fused at feature map level. A convolutional decoder is then used to generate the target image. The result is further refined with another convolutional encoder-decoder network with the initial result and the original image as inputs. We evaluate the proposed method on MultiPIE dataset for three sub-tasks, that is, rotating faces, changing illumination and image inpainting. We show that the method is able to generate realistic images for the three tasks.
