PEAN: 3D Hand Pose Estimation Adversarial Network
Linhui Sun; Yifan Zhang; Jian Cheng; Hanqing Lu
2021-01
会议名称In Proceedings of the International Conference on Pattern Recognition
会议日期2021-1
会议地点Milan, Italy
摘要
Despite recent emerging research attention, 3D hand pose estimation still suffers from the problems of predicting inaccurate or invalid poses which conflict with physical and kinematic constraints. To address these problems, we propose a novel 3D hand pose estimation adversarial network (PEAN) which can implicitly utilize such constraints to regularize the prediction in an adversarial learning framework. PEAN contains two parts: a 3D hierarchical estimation network (3DHNet) to predict hand pose, which decouples the task into multiple subtasks with a hierarchical structure; a pose discrimination network (PDNet) to judge the reasonableness of the estimated 3D hand pose, which back-propagates the constraints to the estimation network. During the adversarial learning process, PDNet is expected to distinguish the estimated 3D hand pose and the ground truth, while 3DHNet is expected to estimate more valid pose to confuse PDNet. In this way, 3DHNet is capable of generating 3D poses with accurate positions and adaptively adjusting the invalid poses without additional prior knowledge. Experiments show that the proposed 3DHNet does a good job in predicting hand poses, and introducing PDNet to 3DHNet does further improve the accuracy and reasonableness of the predicted results. As a result, the proposed PEAN achieves the state-of-the-art performance on three public hand pose estimation datasets.
收录类别EI
七大方向——子方向分类图像视频处理与分析
国重实验室规划方向分类视觉信息处理
是否有论文关联数据集需要存交
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/54538
专题紫东太初大模型研究中心_图像与视频分析
通讯作者Yifan Zhang
作者单位1.NLPR & AIRIA, Institute of Automation, Chinese Academy of Sciences
2.School of Artificial Intelligence, University of Chinese Academy of Sciences, 100049, Beijing, China
推荐引用方式
GB/T 7714
Linhui Sun,Yifan Zhang,Jian Cheng,et al. PEAN: 3D Hand Pose Estimation Adversarial Network[C],2021.
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