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.
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.
The pose with the largest geometric consistency with the query image, e. g., in the form of an inlier count, is then selected in a second stage.
In this work we address the problem of finding reliable pixel-level correspondences under difficult imaging conditions.
Ranked #10 on Image Matching on IMC PhotoTourism
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)
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.
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.