Shengtao Xiao

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We propose a novel cascaded regression framework for face alignment based on a deep convolutional neural network (CNN). In most existing cascaded regression methods, the shape-indexed features are either obtained by hand-crafted visual descriptors or by leaning from the shallow models. This setting may be suboptimal for the face alignment task. To solve(More)
In this technical demonstration, we propose a face swapping framework, which is able to interactively change the appearance of a face in the wild to a different person/creature's face in real time on a mobile device. To realize this objective, we develop a deep learning-based face detector which is able to accurately detect faces in the wild. Our face(More)
—We propose a novel end-to-end deep architecture for face landmark detection, based on a deep convolutional and deconvolutional network followed by carefully designed recurrent network structures. The pipeline of this architecture consists of three parts. Through the first part, we encode an input face image to resolution-preserved deconvolutional feature(More)