Bare-hand Depth Inpainting for 3D Tracking of Hand Interacting with Object

We propose a 3D hand tracking system using bare-hand depth inpainting from an RGB-depth image for a hand interacting with an object. The effectiveness of most existing hand-object tracking methods is impeded by the insufficiency of data, which do not include hand data occluded by the object, and their reliance on the information inferred from assuming the specific object type. We generate a sufficiently accurate bare-hand depth image from a hand interacting with an object using a conditional generative adversarial network, which is trained using the synthesized 2D silhouettes of the object to learn the morphology of the hand. We evaluate the proposed approach using a hierarchical particle filter-based hand tracker and prove that our approach utilizing the bare-hand tracker in the handobject interaction dataset achieve state-of-the-art performance. The generalization of our work will enable visual-tactile interaction that is more natural in various wearable augmented reality applications.