no code implementations • ECCV 2020 • Vikramjit Sidhu, Edgar Tretschk, Vladislav Golyanik, Antonio Agudo, Christian Theobalt
We introduce the first dense neural non-rigid structure from motion (N-NRSfM) approach, which can be trained end-to-end in an unsupervised manner from 2D point tracks.
no code implementations • 12 Apr 2024 • Marc Gutiérrez-Pérez, Antonio Agudo
Broadcast sports field registration is traditionally addressed as a homography estimation task, mapping the visible image area to a planar field model, predominantly focusing on the main camera shot.
2 code implementations • 23 Feb 2024 • Daniel Ordoñez-Apraez, Giulio Turrisi, Vladimir Kostic, Mario Martin, Antonio Agudo, Francesc Moreno-Noguer, Massimiliano Pontil, Claudio Semini, Carlos Mastalli
We present a comprehensive framework for studying and leveraging morphological symmetries in robotic systems.
no code implementations • 13 Dec 2023 • Guénolé Fiche, Simon Leglaive, Xavier Alameda-Pineda, Antonio Agudo, Francesc Moreno-Noguer
Instead of predicting body model parameters or 3D vertex coordinates, our focus is on forecasting the proposed discrete latent representation, which can be decoded into a registered human mesh.
no code implementations • 26 Jun 2023 • Raül Pérez-Gonzalo, Andreas Espersen, Antonio Agudo
With the relentless growth of the wind industry, there is an imperious need to design automatic data-driven solutions for wind turbine maintenance.
2 code implementations • 21 Feb 2023 • Daniel Ordonez-Apraez, Mario Martin, Antonio Agudo, Francesc Moreno-Noguer
We present a comprehensive study on discrete morphological symmetries of dynamical systems, which are commonly observed in biological and artificial locomoting systems, such as legged, swimming, and flying animals/robots/virtual characters.
no code implementations • 11 Apr 2022 • Nicolas Ugrinovic, Adria Ruiz, Antonio Agudo, Alberto Sanfeliu, Francesc Moreno-Noguer
For this purpose, we build a residual-like permutation-invariant network that successfully refines potentially corrupted initial 3D poses estimated by an off-the-shelf detector.
3D Multi-Person Pose Estimation (absolute) 3D Multi-Person Pose Estimation (root-relative) +1
1 code implementation • 18 Mar 2022 • Jianxiong Shen, Antonio Agudo, Francesc Moreno-Noguer, Adria Ruiz
For this purpose, our method learns a distribution over all possible radiance fields modelling which is used to quantify the uncertainty associated with the modelled scene.
1 code implementation • 2 Nov 2021 • Nicolas Ugrinovic, Adria Ruiz, Antonio Agudo, Alberto Sanfeliu, Francesc Moreno-Noguer
We address the problem of multi-person 3D body pose and shape estimation from a single image.
no code implementations • 28 Oct 2021 • Daniel Ordonez-Apraez, Antonio Agudo, Francesc Moreno-Noguer, Mario Martin
We present experimental results showing that even in a model-free setup and with a simple reactive control architecture, the learned policies can generate realistic and energy-efficient locomotion gaits for a bipedal and a quadrupedal robot.
no code implementations • 6 Oct 2021 • Ruijie Ren, Mohit Gurnani Rajesh, Jordi Sanchez-Riera, Fan Zhang, Yurun Tian, Antonio Agudo, Yiannis Demiris, Krystian Mikolajczyk, Francesc Moreno-Noguer
We show that training our network solely with synthetic data and the proposed DA yields results competitive with models trained on real data.
no code implementations • 5 Sep 2021 • Jianxiong Shen, Adria Ruiz, Antonio Agudo, Francesc Moreno-Noguer
In this context, we propose Stochastic Neural Radiance Fields (S-NeRF), a generalization of standard NeRF that learns a probability distribution over all the possible radiance fields modeling the scene.
no code implementations • CVPR 2021 • Alexander Vakhitov, Luis Ferraz Colomina, Antonio Agudo, Francesc Moreno-Noguer
The new PnP(L) methods outperform the state-of-the-art on real data in isolation, showing an increase in mean translation accuracy by 18% on a representative subset of KITTI, while the new uncertain refinement improves pose accuracy for most of the solvers, e. g. decreasing mean translation error for the EPnP by 16% compared to the standard refinement on the same dataset.
no code implementations • ICCV 2021 • Adria Ruiz, Antonio Agudo, Francesc Moreno
Attribution map visualization has arisen as one of the most effective techniques to understand the underlying inference process of Convolutional Neural Networks.
no code implementations • CVPR 2018 • Albert Pumarola, Antonio Agudo, Alberto Sanfeliu, Francesc Moreno-Noguer
Given an input image of a person and a desired pose represented by a 2D skeleton, our model renders the image of the same person under the new pose, synthesizing novel views of the parts visible in the input image and hallucinating those that are not seen.
no code implementations • CVPR 2018 • Albert Pumarola, Antonio Agudo, Lorenzo Porzi, Alberto Sanfeliu, Vincent Lepetit, Francesc Moreno-Noguer
We propose a method for predicting the 3D shape of a deformable surface from a single view.
7 code implementations • ECCV 2018 • Albert Pumarola, Antonio Agudo, Aleix M. Martinez, Alberto Sanfeliu, Francesc Moreno-Noguer
Recent advances in Generative Adversarial Networks (GANs) have shown impressive results for task of facial expression synthesis.
no code implementations • CVPR 2018 • Antonio Agudo, Melcior Pijoan, Francesc Moreno-Noguer
This paper introduces an approach to simultaneously estimate 3D shape, camera pose, and object and type of deformation clustering, from partial 2D annotations in a multi-instance collection of images.
no code implementations • CVPR 2017 • Antonio Agudo, Francesc Moreno-Noguer
We present an approach to reconstruct the 3D shape of multiple deforming objects from incomplete 2D trajectories acquired by a single camera.
no code implementations • ICCV 2015 • Antonio Agudo, Francesc Moreno-Noguer
In this paper, we address the problem of simultaneously recovering the 3D shape and pose of a deformable and potentially elastic object from 2D motion.
no code implementations • CVPR 2015 • Antonio Agudo, Francesc Moreno-Noguer
In this paper, we propose a sequential solution to simultaneously estimate camera pose and non-rigid 3D shape from a monocular video.
no code implementations • CVPR 2014 • Antonio Agudo, Lourdes Agapito, Begona Calvo, Jose M. M. Montiel
We propose an online solution to non-rigid structure from motion that performs camera pose and 3D shape estimation of highly deformable surfaces on a frame-by-frame basis.