3D Hand Shape and Pose Estimation from a Single RGB Image

Understand

This work addresses a novel and challenging problem of estimating the full 3D hand shape and pose from a single RGB image.

• Most current methods in 3D hand analysis from monocular RGB images only focus on estimating the 3D locations of hand keypoints, which cannot fully express the 3D shape of hand.
• In contrast, we propose a Graph Convolutional Neural Network (Graph CNN) based method to reconstruct a full 3D mesh of hand surface that contains richer information of both 3D hand shape and pose.
• To train networks with full supervision, we create a large-scale synthetic dataset containing both ground truth 3D meshes and 3D poses.