45 citations · 162 across the 19 of their papers we have counts for
7 papers · 1 filter
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…