Local and Global Point Cloud Reconstruction for 3D Hand Pose Estimation
This work addresses the problem of accurate 3D hand pose estimation for applications like human-computer interaction, though it appears incremental as it builds on existing reconstruction and pose estimation methods.
The paper tackles 3D hand pose estimation from a single RGB image by proposing a pipeline for local and global point cloud reconstruction using a 3D hand template, achieving superior performance in pose estimation and realistic point cloud reconstruction compared to competitors.
This paper addresses the 3D point cloud reconstruction and 3D pose estimation of the human hand from a single RGB image. To that end, we present a novel pipeline for local and global point cloud reconstruction using a 3D hand template while learning a latent representation for pose estimation. To demonstrate our method, we introduce a new multi-view hand posture dataset to obtain complete 3D point clouds of the hand in the real world. Experiments on our newly proposed dataset and four public benchmarks demonstrate the model's strengths. Our method outperforms competitors in 3D pose estimation while reconstructing realistic-looking complete 3D hand point clouds.