3D Hand Shape and Pose from Images in the Wild

Understand

We present in this work the first end-to-end deep learning based method that predicts both 3D hand shape and pose from RGB images in the wild.

• Our network consists of the concatenation of a deep convolutional encoder, and a fixed model-based decoder.
• Given an input image, and optionally 2D joint detections obtained from an independent CNN, the encoder predicts a set of hand and view parameters.
• The decoder has two components: A pre-computed articulated mesh deformation hand model that generates a 3D mesh from the hand parameters, and a re-projection module controlled by the view parameters that projects the generated hand into the image domain.