Publikation

DeepHPS: End-to-end Estimation of 3D Hand Pose and Shape by Learning from Synthetic Depth

Muhammad Jameel Nawaz Malik, Ahmed Elhayek, Fabrizio Nunnari, Kiran Varanasi, Kiarash Tamaddon, Alexis Heloir, Didier Stricker

In: Proceeding of International Conference on 3DVision |. International Conference on 3D Vision (3DVision-2018) September 5-8 Verona Italy IEEE Xplore 10/2018.

Abstrakt

Articulated hand pose and shape estimation is an important problem for vision-based applications such as augmented reality and animation. In contrast to the existing methods which optimize only for joint positions, we propose a fully supervised deep network which learns to jointly estimate a full 3D hand mesh representation and pose from a single depth image. To this end, a CNN architecture is employed to estimate parametric representations i.e. hand pose, bone scales and complex shape parameters. Then, a novel hand pose and shape layer, embedded inside our deep framework, produces 3D joint positions and hand mesh.Lack of sufficient training data with varying hand shapes limits the generalized performance of learning based methods. Also, manually annotating real data is uboptimal. Therefore, we present SynHand5M: a million-scale synthetic dataset with accurate joint annotations, segmentation masks and mesh files of depth maps. Among model based learning (hybrid) methods, we show improved results on our dataset and two of the public benchmarks i.e. NYU and ICVL. Also, by employing a joint training strategy with real and synthetic data, we recover 3D hand mesh and pose from real images in 3.7ms.

Projekte

Weitere Links

Malik2018_3DV_3D_Hand_Pose_and_Shape.pdf (pdf, 4 MB) Malik2018_3DV_3D_Hand_Pose_and_Shape_supp.pdf (pdf, 546 KB)

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence