An end-to-end framework for unconstrained monocular 3D hand pose estimation

作者：

Highlights：

• Our proposed framework can robustly infer 3D hand pose without requiring a prior.

• Novel keypoint-based hand detector robust to confusing background and adjacent hands.

• Two anatomy-based constraints for aiding 3D hand pose estimation network performance.

• An end-to-end pipeline with state-of-the-art performance on several datasets.

摘要

•Our proposed framework can robustly infer 3D hand pose without requiring a prior.•Novel keypoint-based hand detector robust to confusing background and adjacent hands.•Two anatomy-based constraints for aiding 3D hand pose estimation network performance.•An end-to-end pipeline with state-of-the-art performance on several datasets.

论文关键词：Hand detection,Hand tracking,Hand pose estimation,Computer vision,Deep learning

论文评审过程：Received 26 February 2020, Revised 11 January 2021, Accepted 11 February 2021, Available online 16 February 2021, Version of Record 26 February 2021.

论文官网地址：https://doi.org/10.1016/j.patcog.2021.107892