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Found 8 papers, 1 papers with code
ShapeGraFormer: GraFormer-Based Network for Hand-Object Reconstruction from a Single Depth Map
In addition, we show the impact of adding another GraFormer component that refines the reconstructed shapes based on the hand-object interactions and its ability to reconstruct more accurate object shapes.
THOR-Net: End-to-end Graformer-based Realistic Two Hands and Object Reconstruction with Self-supervision
In the features extraction stage, a Keypoint RCNN is used to extract 2D poses, features maps, heatmaps, and bounding boxes from a monocular RGB image.
HandVoxNet++: 3D Hand Shape and Pose Estimation using Voxel-Based Neural Networks
To address the limitations of the existing methods, we develop HandVoxNet++, i. e., a voxel-based deep network with 3D and graph convolutions trained in a fully supervised manner.
HandVoxNet: Deep Voxel-Based Network for 3D Hand Shape and Pose Estimation from a Single Depth Map
The input to our method is a 3D voxelized depth map, and we rely on two hand shape representations.
Structure from Articulated Motion: Accurate and Stable Monocular 3D Reconstruction without Training Data
SfAM is highly robust to noisy 2D annotations, generalizes to arbitrary objects and does not rely on training data, which is shown in extensive experiments on public benchmarks and real video sequences.
DeepHPS: End-to-end Estimation of 3D Hand Pose and Shape by Learning from Synthetic Depth
Also, by employing a joint training strategy with real and synthetic data, we recover 3D hand mesh and pose from real images in 3. 7ms.
Simultaneous Hand Pose and Skeleton Bone-Lengths Estimation from a Single Depth Image
Articulated hand pose estimation is a challenging task for human-computer interaction.
Efficient ConvNet-Based Marker-Less Motion Capture in General Scenes With a Low Number of Cameras
Our approach unites a discriminative image-based joint detection method with a model-based generative motion tracking algorithm through a combined pose optimization energy.
