499 citations · 2.2k across the 41 of their papers we have counts for
6 papers · 1 filter
Synthetic Training for Accurate 3D Human Pose and Shape Estimation in the Wild
Akash Sengupta, Ignas Budvytis, Roberto Cipolla
This paper addresses the problem of monocular 3D human shape and pose estimation from an RGB image. Despite great progress in this field in terms of pose prediction accuracy, state…
A CNN Based Approach for the Near-Field Photometric Stereo Problem
Fotios Logothetis, Ignas Budvytis, Roberto Mecca +1
Reconstructing the 3D shape of an object using several images under different light sources is a very challenging task, especially when realistic assumptions such as light propagat…
Embodied Visual Navigation with Automatic Curriculum Learning in Real Environments
Steven D. Morad, Roberto Mecca, Rudra P. K. Poudel +2
We present NavACL, a method of automatic curriculum learning tailored to the navigation task. NavACL is simple to train and efficiently selects relevant tasks using geometric featu…
PX-NET: Simple and Efficient Pixel-Wise Training of Photometric Stereo Networks
Fotios Logothetis, Ignas Budvytis, Roberto Mecca +1
Retrieving accurate 3D reconstructions of objects from the way they reflect light is a very challenging task in computer vision. Despite more than four decades since the definition…
Who Left the Dogs Out? 3D Animal Reconstruction with Expectation Maximization in the Loop
Benjamin Biggs, Oliver Boyne, James Charles +2
We introduce an automatic, end-to-end method for recovering the 3D pose and shape of dogs from monocular internet images. The large variation in shape between dog breeds, significa…
Predicting Semantic Map Representations from Images using Pyramid Occupancy Networks
Thomas Roddick, Roberto Cipolla
Autonomous vehicles commonly rely on highly detailed birds-eye-view maps of their environment, which capture both static elements of the scene such as road layout as well as dynami…