8 accepted papers
In this paper, we present our method for neural face reenactment, called HyperReenact, that aims to generate realistic talking head images of a source identity, driven by a target facial pose. Existing state-of-the-art face reenactment methods train controllable generative models that learn to synth…
Anomaly detection is a challenging problem, mainly due to the lack of a sufficient set of abnormal samples that represents every possible anomaly. Therefore unsupervised methods are employed to model normality and anomaly is detected as an outlier to such models. This paper introduces Fourier Trans-…
In this work, we pose the question whether it is possible to design and train an autoencoder model in an end-to-end fashion to learn representations in the multivariate Bernoulli latent space, and achieve performance comparable with the state-of-the-art variational methods. Moreover, we investigate…
Deep Neural networks (DNN) dominate the state of art results in computer vision (CV) and other fields. One of the primary reasons why DNN outperform existing algorithms is that these produce superior results when more labelled data are used, unlike classic CV techniques. Nonetheless, it is well know…
In this paper we present the design and evaluation of an end to end trainable, deep neural network with a visual attention mechanism for memorability estimation in still images. We analyze the suitability of transfer learning of deep models from image classification to the memorability task. Further…
3D face reconstruction is a fundamental Computer Vision problem of extraordinary difficulty. Current systems often assume the availability of multiple facial images (sometimes from the same subject) as input, and must address a number of methodological challenges such as establishing dense correspon…
In this work, the focus is on gradient-based correlation schemes which constitute an alternative to phase correlation for FFT-based motion estimation. In particular, our contribution is threefold. First, we present an analysis which highlights the key features of gradient schemes. Second, we introdu…
In this paper we address the notion of risk assessment of three dimensional scenes. Furthermore through the use of local feature recognition techniques and machine learning we perform this analysis on real time point cloud recordings. We provide a definition of risk and potential hazards that incorp…