DIREG3D: DIrectly REGress 3D Hands from Multiple Cameras
This addresses hand tracking for applications like VR or robotics, but appears incremental as it builds on existing regression and multi-view fusion techniques.
The paper tackles 3D hand tracking by proposing DIREG3D, a framework that regresses hand mesh parameters from monocular or multi-view camera inputs, achieving reliable 3D poses in camera space.
In this paper, we present DIREG3D, a holistic framework for 3D Hand Tracking. The proposed framework is capable of utilizing camera intrinsic parameters, 3D geometry, intermediate 2D cues, and visual information to regress parameters for accurately representing a Hand Mesh model. Our experiments show that information like the size of the 2D hand, its distance from the optical center, and radial distortion is useful for deriving highly reliable 3D poses in camera space from just monocular information. Furthermore, we extend these results to a multi-view camera setup by fusing features from different viewpoints.