2 citations · 2 across the 2 of their papers we have counts for
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The Florence 4D Facial Expression Dataset
F. Principi, S. Berretti, C. Ferrari +3
Human facial expressions change dynamically, so their recognition / analysis should be conducted by accounting for the temporal evolution of face deformations either in 2D or 3D. W…
3D Skeleton-based Human Motion Prediction with Manifold-Aware GAN
Baptiste Chopin, Naima Otberdout, Mohamed Daoudi +1
In this work we propose a novel solution for 3D skeleton-based human motion prediction. The objective of this task consists in forecasting future human poses based on a prior skele…
Human Motion Prediction Using Manifold-Aware Wasserstein GAN
Baptiste Chopin, Naima Otberdout, Mohamed Daoudi +1
Human motion prediction aims to forecast future human poses given a prior pose sequence. The discontinuity of the predicted motion and the performance deterioration in long-term ho…
Dynamic Facial Expression Generation on Hilbert Hypersphere with Conditional Wasserstein Generative Adversarial Nets
Naima Otberdout, Mohamed Daoudi, Anis Kacem +2
In this work, we propose a novel approach for generating videos of the six basic facial expressions given a neutral face image. We propose to exploit the face geometry by modeling…
Automatic Analysis of Facial Expressions Based on Deep Covariance Trajectories
Naima Otberdout, Anis Kacem, Mohamed Daoudi +2
In this paper, we propose a new approach for facial expression recognition using deep covariance descriptors. The solution is based on the idea of encoding local and global Deep Co…
Deep Covariance Descriptors for Facial Expression Recognition
Naima Otberdout, Anis Kacem, Mohamed Daoudi +2
In this paper, covariance matrices are exploited to encode the deep convolutional neural networks (DCNN) features for facial expression recognition. The space geometry of the covar…