Search Results for author: Ignacio Rocco

Found 8 papers, 5 papers with code

BodyMap: Learning Full-Body Dense Correspondence Map

no code implementations18 May 2022 Anastasia Ianina, Nikolaos Sarafianos, Yuanlu Xu, Ignacio Rocco, Tony Tung

Dense correspondence between humans carries powerful semantic information that can be utilized to solve fundamental problems for full-body understanding such as in-the-wild surface matching, tracking and reconstruction.

Neural Rendering Novel View Synthesis

Reconstructing and grounding narrated instructional videos in 3D

no code implementations9 Sep 2021 Dimitri Zhukov, Ignacio Rocco, Ivan Laptev, Josef Sivic, Johannes L. Schönberger, Bugra Tekin, Marc Pollefeys

Contrary to the standard scenario of instance-level 3D reconstruction, where identical objects or scenes are present in all views, objects in different instructional videos may have large appearance variations given varying conditions and versions of the same product.

3D Reconstruction

Efficient Neighbourhood Consensus Networks via Submanifold Sparse Convolutions

1 code implementation ECCV 2020 Ignacio Rocco, Relja Arandjelović, Josef Sivic

In this work we target the problem of estimating accurately localised correspondences between a pair of images.

Neighbourhood Consensus Networks

3 code implementations NeurIPS 2018 Ignacio Rocco, Mircea Cimpoi, Relja Arandjelović, Akihiko Torii, Tomas Pajdla, Josef Sivic

Second, we demonstrate that the model can be trained effectively from weak supervision in the form of matching and non-matching image pairs without the need for costly manual annotation of point to point correspondences.

Ranked #2 on Semantic correspondence on PF-PASCAL (PCK (weak) metric)

Semantic correspondence Visual Localization

End-to-end weakly-supervised semantic alignment

2 code implementations CVPR 2018 Ignacio Rocco, Relja Arandjelović, Josef Sivic

We tackle the task of semantic alignment where the goal is to compute dense semantic correspondence aligning two images depicting objects of the same category.

Semantic correspondence

Convolutional neural network architecture for geometric matching

5 code implementations CVPR 2017 Ignacio Rocco, Relja Arandjelović, Josef Sivic

We address the problem of determining correspondences between two images in agreement with a geometric model such as an affine or thin-plate spline transformation, and estimating its parameters.

Geometric Matching

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