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Found 8 papers, 1 papers with code
Generating Multiple 4D Expression Transitions by Learning Face Landmark Trajectories
We thus propose a new model that generates transitions between different expressions, and synthesizes long and composed 4D expressions.
Interaction Transformer for Human Reaction Generation
To address this limitation, we propose a novel interaction Transformer (InterFormer) consisting of a Transformer network with both temporal and spatial attention.
3D Skeleton-based Human Motion Prediction with Manifold-Aware GAN
Using such a compact representation avoids error accumulation and provides robust representation for long-term prediction while ensuring the smoothness and the coherence of the whole motion.
Human Motion Prediction Using Manifold-Aware Wasserstein GAN
Human motion prediction aims to forecast future human poses given a prior pose sequence.
Sparse to Dense Dynamic 3D Facial Expression Generation
This allows us to learn how the motion of a sparse set of landmarks influences the deformation of the overall face surface, independently from the identity.
Dynamic Facial Expression Generation on Hilbert Hypersphere with Conditional Wasserstein Generative Adversarial Nets
In this work, we propose a novel approach for generating videos of the six basic facial expressions given a neutral face image.
Automatic Analysis of Facial Expressions Based on Deep Covariance Trajectories
In this paper, we propose a new approach for facial expression recognition using deep covariance descriptors.
Deep Covariance Descriptors for Facial Expression Recognition
In this paper, covariance matrices are exploited to encode the deep convolutional neural networks (DCNN) features for facial expression recognition.
