Search Results for author:
Found 3 papers, 1 papers with code
K-Order Graph-oriented Transformer with GraAttention for 3D Pose and Shape Estimation
We propose a novel attention-based 2D-to-3D pose estimation network for graph-structured data, named KOG-Transformer, and a 3D pose-to-shape estimation network for hand data, named GASE-Net.
GraFormer: Graph-Oriented Transformer for 3D Pose Estimation
In 2D-to-3D pose estimation, it is important to exploit the spatial constraints of 2D joints, but it is not yet well modeled.
GraFormer: Graph Convolution Transformer for 3D Pose Estimation
Exploiting relations among 2D joints plays a crucial role yet remains semi-developed in 2D-to-3D pose estimation.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
