HandVoxNet: Deep Voxel-Based Network for 3D Hand Shape and Pose Estimation from a Single Depth Map

Understand

3D hand shape and pose estimation from a single depth map is a new and challenging computer vision problem with many applications.

• The state-of-the-art methods directly regress 3D hand meshes from 2D depth images via 2D convolutional neural networks, which leads to artefacts in the estimations due to perspective distortions in the images.
• In contrast, we propose a novel architecture with 3D convolutions trained in a weakly-supervised manner.
• The input to our method is a 3D voxelized depth map, and we rely on two hand shape representations.