336 citations · 1.4k across the 70 of their papers we have counts for
33 papers · 1 filter
MEGA: Masked Generative Autoencoder for Human Mesh Recovery
Guénolé Fiche, Simon Leglaive, Xavier Alameda-Pineda +1
Human Mesh Recovery (HMR) from a single RGB image is a highly ambiguous problem, as an infinite set of 3D interpretations can explain the 2D observation equally well. Nevertheless,…
VQ-HPS: Human Pose and Shape Estimation in a Vector-Quantized Latent Space
Guénolé Fiche, Simon Leglaive, Xavier Alameda-Pineda +2
Previous works on Human Pose and Shape Estimation (HPSE) from RGB images can be broadly categorized into two main groups: parametric and non-parametric approaches. Parametric techn…
On the Effectiveness of LayerNorm Tuning for Continual Learning in Vision Transformers
Thomas De Min, Massimiliano Mancini, Karteek Alahari +2
State-of-the-art rehearsal-free continual learning methods exploit the peculiarities of Vision Transformers to learn task-specific prompts, drastically reducing catastrophic forget…
Semi-supervised learning made simple with self-supervised clustering
Enrico Fini, Pietro Astolfi, Karteek Alahari +4
Self-supervised learning models have been shown to learn rich visual representations without requiring human annotations. However, in many real-world scenarios, labels are partiall…
Motion-DVAE: Unsupervised learning for fast human motion denoising
Guénolé Fiche, Simon Leglaive, Xavier Alameda-Pineda +1
Pose and motion priors are crucial for recovering realistic and accurate human motion from noisy observations. Substantial progress has been made on pose and shape estimation from…
Autoregressive GAN for Semantic Unconditional Head Motion Generation
Louis Airale, Xavier Alameda-Pineda, Stéphane Lathuilière +1
In this work, we address the task of unconditional head motion generation to animate still human faces in a low-dimensional semantic space from a single reference pose. Different f…
Back to MLP: A Simple Baseline for Human Motion Prediction
Wen Guo, Yuming Du, Xi Shen +3
This paper tackles the problem of human motion prediction, consisting in forecasting future body poses from historically observed sequences. State-of-the-art approaches provide goo…
HiT-DVAE: Human Motion Generation via Hierarchical Transformer Dynamical VAE
Xiaoyu Bie, Wen Guo, Simon Leglaive +3
Studies on the automatic processing of 3D human pose data have flourished in the recent past. In this paper, we are interested in the generation of plausible and diverse future hum…
Expression-preserving face frontalization improves visually assisted speech processing
Zhiqi Kang, Mostafa Sadeghi, Radu Horaud +1
Face frontalization consists of synthesizing a frontally-viewed face from an arbitrarily-viewed one. The main contribution of this paper is a frontalization methodology that preser…
Uncertainty-aware Contrastive Distillation for Incremental Semantic Segmentation
Guanglei Yang, Enrico Fini, Dan Xu +5
A fundamental and challenging problem in deep learning is catastrophic forgetting, i.e. the tendency of neural networks to fail to preserve the knowledge acquired from old tasks wh…
Continual Attentive Fusion for Incremental Learning in Semantic Segmentation
Guanglei Yang, Enrico Fini, Dan Xu +5
Over the past years, semantic segmentation, as many other tasks in computer vision, benefited from the progress in deep neural networks, resulting in significantly improved perform…
Self-Supervised Models are Continual Learners
Enrico Fini, Victor G. Turrisi da Costa, Xavier Alameda-Pineda +3
Self-supervised models have been shown to produce comparable or better visual representations than their supervised counterparts when trained offline on unlabeled data at scale. Ho…
Multi-Person Extreme Motion Prediction
Wen Guo, Xiaoyu Bie, Xavier Alameda-Pineda +1
Human motion prediction aims to forecast future poses given a sequence of past 3D skeletons. While this problem has recently received increasing attention, it has mostly been tackl…
TransCenter: Transformers with Dense Representations for Multiple-Object Tracking
Yihong Xu, Yutong Ban, Guillaume Delorme +3
Transformers have proven superior performance for a wide variety of tasks since they were introduced. In recent years, they have drawn attention from the vision community in tasks…
Variational Structured Attention Networks for Deep Visual Representation Learning
Guanglei Yang, Paolo Rota, Xavier Alameda-Pineda +3
Convolutional neural networks have enabled major progresses in addressing pixel-level prediction tasks such as semantic segmentation, depth estimation, surface normal prediction an…
Probabilistic Graph Attention Network with Conditional Kernels for Pixel-Wise Prediction
Dan Xu, Xavier Alameda-Pineda, Wanli Ouyang +3
Multi-scale representations deeply learned via convolutional neural networks have shown tremendous importance for various pixel-level prediction problems. In this paper we present…
PI-Net: Pose Interacting Network for Multi-Person Monocular 3D Pose Estimation
Wen Guo, Enric Corona, Francesc Moreno-Noguer +1
Recent literature addressed the monocular 3D pose estimation task very satisfactorily. In these studies, different persons are usually treated as independent pose instances to esti…
Describe What to Change: A Text-guided Unsupervised Image-to-Image Translation Approach
Yahui Liu, Marco De Nadai, Deng Cai +4
Manipulating visual attributes of images through human-written text is a very challenging task. On the one hand, models have to learn the manipulation without the ground truth of t…
Unsupervised Performance Analysis of 3D Face Alignment with a Statistically Robust Confidence Test
Mostafa Sadeghi, Xavier Alameda-Pineda, Radu Horaud
This paper addresses the problem of analysing the performance of 3D face alignment (3DFA), or facial landmark localization. This task is usually supervised, based on annotated data…
GMM-UNIT: Unsupervised Multi-Domain and Multi-Modal Image-to-Image Translation via Attribute Gaussian Mixture Modeling
Yahui Liu, Marco De Nadai, Jian Yao +3
Unsupervised image-to-image translation (UNIT) aims at learning a mapping between several visual domains by using unpaired training images. Recent studies have shown remarkable suc…
How To Train Your Deep Multi-Object Tracker
Yihong Xu, Aljosa Osep, Yutong Ban +3
The recent trend in vision-based multi-object tracking (MOT) is heading towards leveraging the representational power of deep learning to jointly learn to detect and track objects.…
CANU-ReID: A Conditional Adversarial Network for Unsupervised person Re-IDentification
Guillaume Delorme, Yihong Xu, Stephane Lathuilière +2
Unsupervised person re-ID is the task of identifying people on a target data set for which the ID labels are unavailable during training. In this paper, we propose to unify two tre…
Variational Bayesian Inference for Audio-Visual Tracking of Multiple Speakers
Yutong Ban, Xavier Alameda-Pineda, Laurent Girin +1
In this paper we address the problem of tracking multiple speakers via the fusion of visual and auditory information. We propose to exploit the complementary nature of these two mo…
DeepGUM: Learning Deep Robust Regression with a Gaussian-Uniform Mixture Model
Stéphane Lathuilière, Pablo Mesejo, Xavier Alameda-Pineda +1
In this paper, we address the problem of how to robustly train a ConvNet for regression, or deep robust regression. Traditionally, deep regression employs the L2 loss function, kno…