97 citations · 328 across the 24 of their papers we have counts for
27 papers
ModaFlow: Modality-Aware Flow Matching for High-Fidelity Virtual Try-On
Xiangyu Sai, Meysam Madadi, Sergio Escalera +1
Image-based virtual try-on has emerged as a compelling task in e-commerce and augmented reality, yet existing methods struggle to simultaneously preserve fine garment semantics and…
What Matters in Virtual Try-Off? Dual-UNet Diffusion Model For Garment Reconstruction
Loc-Phat Truong, Meysam Madadi, Sergio Escalera
Virtual Try-On (VTON) has seen rapid advancements, providing a strong foundation for generative fashion tasks. However, the inverse problem, Virtual Try-Off (VTOFF)-aimed at recons…
MV-Fashion: Towards Enabling Virtual Try-On and Size Estimation with Multi-View Paired Data
Hunor Laczkó, Libang Jia, Loc-Phat Truong +4
Existing 4D human datasets fall short for fashion-specific research, lacking either realistic garment dynamics or task-specific annotations. Synthetic datasets suffer from a realis…
Multimodal classification of forest biodiversity potential from 2D orthophotos and 3D airborne laser scanning point clouds
Simon B. Jensen, Stefan Oehmcke, Andreas Møgelmose +4
Assessment of forest biodiversity is crucial for ecosystem management and conservation. While traditional field surveys provide high-quality assessments, they are labor-intensive a…
A Generative Multi-Resolution Pyramid and Normal-Conditioning 3D Cloth Draping
Hunor Laczkó, Meysam Madadi, Sergio Escalera +1
RGB cloth generation has been deeply studied in the related literature, however, 3D garment generation remains an open problem. In this paper, we build a conditional variational au…
Machine learning-based characterization of hydrochar from biomass: Implications for sustainable energy and material production
Alireza Shafizadeh, Hossein Shahbeik, Shahin Rafiee +7
Hydrothermal carbonization (HTC) is a process that converts biomass into versatile hydrochar without the need for prior drying. The physicochemical properties of hydrochar are infl…
Blowing in the Wind: CycleNet for Human Cinemagraphs from Still Images
Hugo Bertiche, Niloy J. Mitra, Kuldeep Kulkarni +5
Cinemagraphs are short looping videos created by adding subtle motions to a static image. This kind of media is popular and engaging. However, automatic generation of cinemagraphs…
Neural Cloth Simulation
Hugo Bertiche, Meysam Madadi, Sergio Escalera
We present a general framework for the garment animation problem through unsupervised deep learning inspired in physically based simulation. Existing trends in the literature alrea…
Predicting the generalization gap in neural networks using topological data analysis
Rubén Ballester, Xavier Arnal Clemente, Carles Casacuberta +3
Understanding how neural networks generalize on unseen data is crucial for designing more robust and reliable models. In this paper, we study the generalization gap of neural netwo…
Winning solutions and post-challenge analyses of the ChaLearn AutoDL challenge 2019
Zhengying Liu, Adrien Pavao, Zhen Xu +22
This paper reports the results and post-challenge analyses of ChaLearn's AutoDL challenge series, which helped sorting out a profusion of AutoML solutions for Deep Learning (DL) th…
Multi-Task Classification of Sewer Pipe Defects and Properties using a Cross-Task Graph Neural Network Decoder
Joakim Bruslund Haurum, Meysam Madadi, Sergio Escalera +1
The sewerage infrastructure is one of the most important and expensive infrastructures in modern society. In order to efficiently manage the sewerage infrastructure, automated sewe…
Neural Implicit Surfaces for Efficient and Accurate Collisions in Physically Based Simulations
Hugo Bertiche, Meysam Madadi, Sergio Escalera
Current trends in the computer graphics community propose leveraging the massive parallel computational power of GPUs to accelerate physically based simulations. Collision detectio…
ChaLearn Looking at People: Inpainting and Denoising challenges
Sergio Escalera, Marti Soler, Stephane Ayache +6
Dealing with incomplete information is a well studied problem in the context of machine learning and computational intelligence. However, in the context of computer vision, the pro…
Deep unsupervised 3D human body reconstruction from a sparse set of landmarks
Meysam Madadi, Hugo Bertiche, Sergio Escalera
In this paper we propose the first deep unsupervised approach in human body reconstruction to estimate body surface from a sparse set of landmarks, so called DeepMurf. We apply a d…
PBNS: Physically Based Neural Simulator for Unsupervised Garment Pose Space Deformation
Hugo Bertiche, Meysam Madadi, Sergio Escalera
We present a methodology to automatically obtain Pose Space Deformation (PSD) basis for rigged garments through deep learning. Classical approaches rely on Physically Based Simulat…
DeePSD: Automatic Deep Skinning And Pose Space Deformation For 3D Garment Animation
Hugo Bertiche, Meysam Madadi, Emilio Tylson +1
We present a novel solution to the garment animation problem through deep learning. Our contribution allows animating any template outfit with arbitrary topology and geometric comp…
Computing the Testing Error without a Testing Set
Ciprian Corneanu, Meysam Madadi, Sergio Escalera +1
Deep Neural Networks (DNNs) have revolutionized computer vision. We now have DNNs that achieve top (performance) results in many problems, including object recognition, facial expr…
Cross-ethnicity Face Anti-spoofing Recognition Challenge: A Review
Ajian Liu, Xuan Li, Jun Wan +12
Face anti-spoofing is critical to prevent face recognition systems from a security breach. The biometrics community has %possessed achieved impressive progress recently due the exc…
CLOTH3D: Clothed 3D Humans
Hugo Bertiche, Meysam Madadi, Sergio Escalera
This work presents CLOTH3D, the first big scale synthetic dataset of 3D clothed human sequences. CLOTH3D contains a large variability on garment type, topology, shape, size, tightn…
Multi-task human analysis in still images: 2D/3D pose, depth map, and multi-part segmentation
Daniel Sánchez, Marc Oliu, Meysam Madadi +2
While many individual tasks in the domain of human analysis have recently received an accuracy boost from deep learning approaches, multi-task learning has mostly been ignored due…
SMPLR: Deep SMPL reverse for 3D human pose and shape recovery
Meysam Madadi, Hugo Bertiche, Sergio Escalera
Current state-of-the-art in 3D human pose and shape recovery relies on deep neural networks and statistical morphable body models, such as the Skinned Multi-Person Linear model (SM…
From 2D to 3D Geodesic-based Garment Matching
Meysam Madadi, Egils Avots, Sergio Escalera +3
A new approach for 2D to 3D garment retexturing is proposed based on Gaussian mixture models and thin plate splines (TPS). An automatically segmented garment of an individual is ma…
Deep Structure Inference Network for Facial Action Unit Recognition
Ciprian A. Corneanu, Meysam Madadi, Sergio Escalera
Facial expressions are combinations of basic components called Action Units (AU). Recognizing AUs is key for developing general facial expression analysis. In recent years, most ef…
Explaining First Impressions: Modeling, Recognizing, and Explaining Apparent Personality from Videos
Hugo Jair Escalante, Heysem Kaya, Albert Ali Salah +14
Explainability and interpretability are two critical aspects of decision support systems. Within computer vision, they are critical in certain tasks related to human behavior analy…