145 citations · 224 across the 24 of their papers we have counts for
6 papers · 1 filter
Progressive Fusion for Unsupervised Binocular Depth Estimation using Cycled Networks
Andrea Pilzer, Stéphane Lathuilière, Dan Xu +3
Recent deep monocular depth estimation approaches based on supervised regression have achieved remarkable performance. However, they require costly ground truth annotations during…
Attention-based Fusion for Multi-source Human Image Generation
Stéphane Lathuilière, Enver Sangineto, Aliaksandr Siarohin +1
We present a generalization of the person-image generation task, in which a human image is generated conditioned on a target pose and a set X of source appearance images. In this w…
Budget-Aware Adapters for Multi-Domain Learning
Rodrigo Berriel, Stéphane Lathuilière, Moin Nabi +4
Multi-Domain Learning (MDL) refers to the problem of learning a set of models derived from a common deep architecture, each one specialized to perform a task in a certain domain (e…
Appearance and Pose-Conditioned Human Image Generation using Deformable GANs
Aliaksandr Siarohin, Stéphane Lathuilière, Enver Sangineto +1
In this paper, we address the problem of generating person images conditioned on both pose and appearance information. Specifically, given an image xa of a person and a target pose…
Refine and Distill: Exploiting Cycle-Inconsistency and Knowledge Distillation for Unsupervised Monocular Depth Estimation
Andrea Pilzer, Stéphane Lathuilière, Nicu Sebe +1
Nowadays, the majority of state of the art monocular depth estimation techniques are based on supervised deep learning models. However, collecting RGB images with associated depth…
Online Adaptation through Meta-Learning for Stereo Depth Estimation
Zhenyu Zhang, Stéphane Lathuilière, Andrea Pilzer +3
In this work, we tackle the problem of online adaptation for stereo depth estimation, that consists in continuously adapting a deep network to a target video recordedin an environm…