Search Results for author:
Found 27 papers, 10 papers with code
Beyond Fixed Topologies: Unregistered Training and Comprehensive Evaluation Metrics for 3D Talking Heads
Our extensive evaluation shows our approach performs favorably compared to fixed topology techniques, setting a new benchmark by offering a versatile and high-fidelity solution for 3D talking head generation.
Generation of Complex 3D Human Motion by Temporal and Spatial Composition of Diffusion Models
In this paper, we address the challenge of generating realistic 3D human motions for action classes that were never seen during the training phase.
Look Into the LITE in Deep Learning for Time Series Classification
In this paper, we present a new architecture for TSC, the Light Inception with boosTing tEchnique (LITE) with only 2. 34% of the number of parameters of the state-of-the-art InceptionTime model, while preserving performance.
Establishing a Unified Evaluation Framework for Human Motion Generation: A Comparative Analysis of Metrics
The development of generative artificial intelligence for human motion generation has expanded rapidly, necessitating a unified evaluation framework.
EmoVOCA: Speech-Driven Emotional 3D Talking Heads
The domain of 3D talking head generation has witnessed significant progress in recent years.
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.
Finding Foundation Models for Time Series Classification with a PreText Task
Over the past decade, Time Series Classification (TSC) has gained an increasing attention.
ShapeDBA: Generating Effective Time Series Prototypes using ShapeDTW Barycenter Averaging
Our approach uses a new form of time series average, the ShapeDTW Barycentric Average.
Learning Landmarks Motion from Speech for Speaker-Agnostic 3D Talking Heads Generation
This paper presents a novel approach for generating 3D talking heads from raw audio inputs.
4DSR-GCN: 4D Video Point Cloud Upsampling using Graph Convolutional Networks
Time varying sequences of 3D point clouds, or 4D point clouds, are now being acquired at an increasing pace in several applications (e. g., LiDAR in autonomous or assisted driving).
An Approach to Multiple Comparison Benchmark Evaluations that is Stable Under Manipulation of the Comparate Set
The measurement of progress using benchmarks evaluations is ubiquitous in computer science and machine learning.
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.
What makes you, you? Analyzing Recognition by Swapping Face Parts
Deep learning advanced face recognition to an unprecedented accuracy.
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.
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.
A Sparse and Locally Coherent Morphable Face Model for Dense Semantic Correspondence Across Heterogeneous 3D Faces
The 3D Morphable Model (3DMM) is a powerful statistical tool for representing 3D face shapes.
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.
Learned 3D Shape Representations Using Fused Geometrically Augmented Images: Application to Facial Expression and Action Unit Detection
Our approach defines an inverse mapping between different geometric descriptors computed on the mesh surface or its down-sampled version, and the corresponding 2D texture image of the mesh, allowing the construction of fused geometrically augmented images (FGAI).
Additional Baseline Metrics for the paper "Extended YouTube Faces: a Dataset for Heterogeneous Open-Set Face Identification"
In this report, we provide additional and corrected results for the paper "Extended YouTube Faces: a Dataset for Heterogeneous Open-Set Face Identification".
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.
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.
Representing 3D Texture on Mesh Manifolds for Retrieval and Recognition Applications
In this paper, we present and experiment a novel approach for representing texture of 3D mesh manifolds using local binary patterns (LBP).
