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Found 6 papers, 3 papers with code
BLSM: A Bone-Level Skinned Model of the Human Mesh
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.
Weakly-Supervised Mesh-Convolutional Hand Reconstruction in the Wild
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.
Slim DensePose: Thrifty Learning from Sparse Annotations and Motion Cues
DensePose supersedes traditional landmark detectors by densely mapping image pixels to body surface coordinates.
Single Image 3D Hand Reconstruction with Mesh Convolutions
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.
DensePose: Dense Human Pose Estimation In The Wild
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
DenseReg: Fully Convolutional Dense Shape Regression In-the-Wild
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.
