2021/09/29 by Yao Lu, Lu, Yao, Walterio Mayol‐Cuevas +1
Computer Science · Engineering · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Hand Gesture Recognition Systems #Human Pose and Action Recognition #Human-Computer Interaction (cs.HC) #Robot Manipulation and Learning
paper · pdf · doi:10.48550/arxiv.2109.14657
openalex publication_date 2021/09/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper, we address the problem of estimating the hand pose from the egocentric view when the hand is interacting with objects. Specifically, we propose a method to label a dataset Ego-Siam which contains the egocentric images pair-wisely. We also use the collected pairwise data to train our encoder-decoder style network which has been proven efficient in. This could bring extra training efficiency and testing accuracy. Our network is lightweight and can be performed with over 30 FPS with an outdated GPU. We demonstrate that our method outperforms Mueller et al. which is the state of the art work dealing with egocentric hand-object interaction problems on the GANerated dataset. To show the ability to preserve the semantic information of our method, we also report the performance of grasp type classification on GUN-71 dataset and outperforms the benchmark by only using the predicted 3-d hand pose.