45 citations · 162 across the 19 of their papers we have counts for
21 papers
A Hierarchical Framework for Collaborative Artificial Intelligence
James L. Crowley, Joëlle L Coutaz, Jasmin Grosinger +5
We propose a hierarchical framework for collaborative intelligent systems. This framework organizes research challenges based on the nature of the collaborative activity and the in…
Robot Navigation Anticipative Strategies in Deep Reinforcement Motion Planning
Óscar Gil, Alberto Sanfeliu
The navigation of robots in dynamic urban environments, requires elaborated anticipative strategies for the robot to avoid collisions with dynamic objects, like bicycles or pedestr…
Weakly and Semi-Supervised Detection, Segmentation and Tracking of Table Grapes with Limited and Noisy Data
Thomas A. Ciarfuglia, Ionut M. Motoi, Leonardo Saraceni +3
Detection, segmentation and tracking of fruits and vegetables are three fundamental tasks for precision agriculture, enabling robotic harvesting and yield estimation applications.…
Humans Social Relationship Classification during Accompaniment
Oscar Castro, Ely Repiso, Anais Garrell +1
This paper presents the design of deep learning architectures which allow to classify the social relationship existing between two people who are walking in a side-by-side formatio…
Body Gesture Recognition to Control a Social Robot
Javier Laplaza, Joan Jaume Oliver, Ramón Romero +2
In this work, we propose a gesture based language to allow humans to interact with robots using their body in a natural way. We have created a new gesture detection model using neu…
Perception-Intention-Action Cycle in Human-Robot Collaborative Tasks
J. E. Dominguez-Vidal, Nicolas Rodriguez, Rene Alquezar +1
In this work we argue that in Human-Robot Collaboration (HRC) tasks, the Perception-Action cycle in HRC tasks can not fully explain the collaborative behaviour of the human and rob…
Permutation-Invariant Relational Network for Multi-person 3D Pose Estimation
Nicolas Ugrinovic, Adria Ruiz, Antonio Agudo +2
The recovery of multi-person 3D poses from a single RGB image is a severely ill-conditioned problem due to the inherent 2D-3D depth ambiguity, inter-person occlusions, and body tru…
Single-view 3D Body and Cloth Reconstruction under Complex Poses
Nicolas Ugrinovic, Albert Pumarola, Alberto Sanfeliu +1
Recent advances in 3D human shape reconstruction from single images have shown impressive results, leveraging on deep networks that model the so-called implicit function to learn t…
Body Size and Depth Disambiguation in Multi-Person Reconstruction from Single Images
Nicolas Ugrinovic, Adria Ruiz, Antonio Agudo +2
We address the problem of multi-person 3D body pose and shape estimation from a single image. While this problem can be addressed by applying single-person approaches multiple time…
Effects of a Social Force Model reward in Robot Navigation based on Deep Reinforcement Learning
Óscar Gil Viyuela, Alberto Sanfeliu
In this paper is proposed an inclusion of the Social Force Model (SFM) into a concrete Deep Reinforcement Learning (RL) framework for robot navigation. These types of techniques ha…
Improving Map Re-localization with Deep 'Movable' Objects Segmentation on 3D LiDAR Point Clouds
Victor Vaquero, Kai Fischer, Francesc Moreno-Noguer +2
Localization and Mapping is an essential component to enable Autonomous Vehicles navigation, and requires an accuracy exceeding that of commercial GPS-based systems. Current odomet…
Human-robot Collaborative Navigation Search using Social Reward Sources
Marc Dalmasso, Anaís Garrell, Pablo Jiménez +1
This paper proposes a Social Reward Sources (SRS) design for a Human-Robot Collaborative Navigation (HRCN) task: human-robot collaborative search. It is a flexible approach capable…
3DPeople: Modeling the Geometry of Dressed Humans
Albert Pumarola, Jordi Sanchez, Gary P. T. Choi +2
Recent advances in 3D human shape estimation build upon parametric representations that model very well the shape of the naked body, but are not appropriate to represent the clothi…
Fast video object segmentation with Spatio-Temporal GANs
Sergi Caelles, Albert Pumarola, Francesc Moreno-Noguer +2
Learning descriptive spatio-temporal object models from data is paramount for the task of semi-supervised video object segmentation. Most existing approaches mainly rely on models…
Geometry-Aware Network for Non-Rigid Shape Prediction from a Single View
Albert Pumarola, Antonio Agudo, Lorenzo Porzi +3
We propose a method for predicting the 3D shape of a deformable surface from a single view. By contrast with previous approaches, we do not need a pre-registered template of the su…
Unsupervised Person Image Synthesis in Arbitrary Poses
Albert Pumarola, Antonio Agudo, Alberto Sanfeliu +1
We present a novel approach for synthesizing photo-realistic images of people in arbitrary poses using generative adversarial learning. Given an input image of a person and a desir…
Hallucinating Dense Optical Flow from Sparse Lidar for Autonomous Vehicles
Victor Vaquero, Alberto Sanfeliu, Francesc Moreno-Noguer
In this paper we propose a novel approach to estimate dense optical flow from sparse lidar data acquired on an autonomous vehicle. This is intended to be used as a drop-in replacem…
Deep Lidar CNN to Understand the Dynamics of Moving Vehicles
Victor Vaquero, Alberto Sanfeliu, Francesc Moreno-Noguer
Perception technologies in Autonomous Driving are experiencing their golden age due to the advances in Deep Learning. Yet, most of these systems rely on the semantically rich infor…
GANimation: Anatomically-aware Facial Animation from a Single Image
Albert Pumarola, Antonio Agudo, Aleix M. Martinez +2
Recent advances in Generative Adversarial Networks (GANs) have shown impressive results for task of facial expression synthesis. The most successful architecture is StarGAN, that c…
Deconvolutional Networks for Point-Cloud Vehicle Detection and Tracking in Driving Scenarios
Victor Vaquero, Ivan del Pino, Francesc Moreno-Noguer +3
Vehicle detection and tracking is a core ingredient for developing autonomous driving applications in urban scenarios. Recent image-based Deep Learning (DL) techniques are obtainin…
Joint Coarse-And-Fine Reasoning for Deep Optical Flow
Victor Vaquero, German Ros, Francesc Moreno-Noguer +2
We propose a novel representation for dense pixel-wise estimation tasks using CNNs that boosts accuracy and reduces training time, by explicitly exploiting joint coarse-and-fine re…