Search Results for author:
Found 30 papers, 3 papers with code
Geometric Generative Models based on Morphological Equivariant PDEs and GANs
Equivariant morphological PDE layers are composed of multiscale dilations and erosions formulated in Riemannian manifolds, while group symmetries are defined on a Lie group.
ScanTalk: 3D Talking Heads from Unregistered Scans
Speech-driven 3D talking heads generation has emerged as a significant area of interest among researchers, presenting numerous challenges.
Basis restricted elastic shape analysis on the space of unregistered surfaces
The use of such bases allows to simplify the representation of the corresponding shape space to a finite dimensional latent space.
Toward Mesh-Invariant 3D Generative Deep Learning with Geometric Measures
3D generative modeling is accelerating as the technology allowing the capture of geometric data is developing.
Transformer-based Self-supervised Multimodal Representation Learning for Wearable Emotion Recognition
Recently, wearable emotion recognition based on peripheral physiological signals has drawn massive attention due to its less invasive nature and its applicability in real-life scenarios.
Bipartite Graph Diffusion Model for Human Interaction Generation
The generation of natural human motion interactions is a hot topic in computer vision and computer animation.
BaRe-ESA: A Riemannian Framework for Unregistered Human Body Shapes
We present Basis Restricted Elastic Shape Analysis (BaRe-ESA), a novel Riemannian framework for human body scan representation, interpolation and extrapolation.
Automatic Estimation of Self-Reported Pain by Trajectory Analysis in the Manifold of Fixed Rank Positive Semi-Definite Matrices
We compared our method to the state-of-the-art on both datasets using different testing protocols, showing the competitiveness of the proposed approach.
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.
3D Shape Sequence of Human Comparison and Classification using Current and Varifolds
In this paper we address the task of the comparison and the classification of 3D shape sequences of human.
Fusion of Physiological and Behavioural Signals on SPD Manifolds with Application to Stress and Pain Detection
Existing multimodal stress/pain recognition approaches generally extract features from different modalities independently and thus ignore cross-modality correlations.
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.
A Riemannian Framework for Analysis of Human Body Surface
We treat a human body surface as a point in a "shape space" equipped with a family of Riemannian metrics.
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.
Magnifying Subtle Facial Motions for Effective 4D Expression Recognition
In this paper, an effective pipeline to automatic 4D Facial Expression Recognition (4D FER) is proposed.
Hybrid Approach for 3D Head Reconstruction: Using Neural Networks and Visual Geometry
Recovering the 3D geometric structure of a face from a single input image is a challenging active research area in computer vision.
Modelling the Statistics of Cyclic Activities by Trajectory Analysis on the Manifold of Positive-Semi-Definite Matrices
In this paper, a model is presented to extract statistical summaries to characterize the repetition of a cyclic body action, for instance a gym exercise, for the purpose of checking the compliance of the observed action to a template one and highlighting the parts of the action that are not correctly executed (if any).
Automatic Estimation of Self-Reported Pain by Interpretable Representations of Motion Dynamics
For each video, pain intensity was measured using the dynamics of facial movement using 66 facial points.
Monocular 3D Head Reconstruction via Prediction and Integration of Normal Vector Field
Reconstructing the geometric structure of a face from a single input image is a challenging active research area in computer vision.
Clinical Depression and Affect Recognition with EmoAudioNet
Automatic analysis of emotions and affects from speech is an inherently challenging problem with a broad range of applications in Human-Computer Interaction (HCI), health informatics, assistive technologies and multimedia retrieval.
Human-Computer Interaction Sound Audio and Speech Processing
Fitting, Comparison, and Alignment of Trajectories on Positive Semi-Definite Matrices with Application to Action Recognition
In this paper, we tackle the problem of action recognition using body skeletons extracted from video sequences.
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.
A Novel Geometric Framework on Gram Matrix Trajectories for Human Behavior Understanding
We derived then geometric and computational tools for rate-invariant analysis and adaptive re-sampling of trajectories, grounding on the Riemannian geometry of the underlying manifold.
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.
A Novel Space-Time Representation on the Positive Semidefinite Cone for Facial Expression Recognition
In this paper, we study the problem of facial expression recognition using a novel space-time geometric representation.
Facial Expression Recognition
Facial Expression Recognition (FER)
Emotion Recognition by Body Movement Representation on the Manifold of Symmetric Positive Definite Matrices
Emotion recognition is attracting great interest for its potential application in a multitude of real-life situations.
A Novel Space-Time Representation on the Positive Semidefinite Con for Facial Expression Recognition
In this paper, we study the problem of facial expression recognition using a novel space-time geometric representation.
Facial Expression Recognition
Facial Expression Recognition (FER)
