DeepHandMesh: A Weakly-supervised Deep Encoder-Decoder Framework for High-fidelity Hand Mesh Modeling

Understand

Human hands play a central role in interacting with other people and objects.

• For realistic replication of such hand motions, high-fidelity hand meshes have to be reconstructed.
• In this study, we firstly propose DeepHandMesh, a weakly-supervised deep encoder-decoder framework for high-fidelity hand mesh modeling.
• We design our system to be trained in an end-to-end and weakly-supervised manner; therefore, it does not require groundtruth meshes.