Search Results for author: Riza Alp Güler

Found 7 papers, 4 papers with code

BLSM: A Bone-Level Skinned Model of the Human Mesh

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.

Unity

MeshPose: Unifying DensePose and 3D Body Mesh reconstruction

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.

Weakly-Supervised Mesh-Convolutional Hand Reconstruction in the Wild

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.

3D Hand Pose Estimation Decoder

Single Image 3D Hand Reconstruction with Mesh Convolutions

1 code implementation4 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.

3D Reconstruction Decoder

DensePose: Dense Human Pose Estimation In The Wild

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.

Monocular 3D Human Pose Estimation

DenseReg: Fully Convolutional Dense Shape Regression In-the-Wild

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.

regression Semantic Segmentation

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