no code implementations • ECCV 2020 • Haoyang Wang, Riza Alp Güler, Iasonas Kokkinos, George Papandreou, Stefanos Zafeiriou
We introduce BLSM, a bone-level skinned model of the human body mesh where bone scales are set prior to template synthesis, rather than the common, inverse practice.
1 code implementation • CVPR 2024 • Eric-Tuan Lê, Antonis Kakolyris, Petros Koutras, Himmy Tam, Efstratios Skordos, George Papandreou, Riza Alp Güler, Iasonas Kokkinos
DensePose provides a pixel-accurate association of images with 3D mesh coordinates, but does not provide a 3D mesh, while Human Mesh Reconstruction (HMR) systems have high 2D reprojection error, as measured by DensePose localization metrics.
3 code implementations • CVPR 2020 • Dominik Kulon, Riza Alp Güler, Iasonas Kokkinos, Michael Bronstein, Stefanos Zafeiriou
We introduce a simple and effective network architecture for monocular 3D hand pose estimation consisting of an image encoder followed by a mesh convolutional decoder that is trained through a direct 3D hand mesh reconstruction loss.
Ranked #22 on 3D Hand Pose Estimation on FreiHAND
no code implementations • CVPR 2019 • Natalia Neverova, James Thewlis, Riza Alp Güler, Iasonas Kokkinos, Andrea Vedaldi
DensePose supersedes traditional landmark detectors by densely mapping image pixels to body surface coordinates.
1 code implementation • 4 May 2019 • Dominik Kulon, Haoyang Wang, Riza Alp Güler, Michael Bronstein, Stefanos Zafeiriou
In this paper, we demonstrate an alternative solution that is based on the idea of encoding images into a latent non-linear representation of meshes.
22 code implementations • CVPR 2018 • Riza Alp Güler, Natalia Neverova, Iasonas Kokkinos
In this work, we establish dense correspondences between RGB image and a surface-based representation of the human body, a task we refer to as dense human pose estimation.
Ranked #2 on Pose Estimation on DensePose-COCO
no code implementations • CVPR 2017 • Riza Alp Güler, George Trigeorgis, Epameinondas Antonakos, Patrick Snape, Stefanos Zafeiriou, Iasonas Kokkinos
As such our network can provide useful correspondence information as a stand-alone system, while when used as an initialization for Statistical Deformable Models we obtain landmark localization results that largely outperform the current state-of-the-art on the challenging 300W benchmark.