基于深度图的三维手部姿态估计
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Keypoint Fusion for RGB-D Based 3D Hand Pose Estimation
State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications Paper Code Abstract Previous 3D hand pose estimation methods primarily rely on a single modality, either RGB or depth, and the comprehensive utilization of the dual modalities has not been extensively explored. RGB and depth data provide complementary information and thus […]
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Two Heads are Better than One: Image-Point Cloud Network for Depth-Based 3D Hand Pose Estimation
State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications Paper Code IPNet utilizes a 2D CNN for visual feature extraction and initial hand pose estimation. Then, IPNet obtains the initial point cloud features through a 2D-3D projection module. Finally, IPNet iteratively updates point features and refines hand pose in the […]
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SA-Fusion: Multimodal Fusion Approach for Web-based Human-Computer Interaction in the Wild
1Beijing University of Posts and Telecommunications, 2State Key Laboratory of Networking and Switching Technology, 3China Mobile Research Institute Paper Abstract Web-based AR technology has broadened human-computer interaction scenes from traditional mechanical devices and flat screens to the real world, resulting in unconstrained environmental challenges such as complex backgrounds, extreme illumination, depth range differences, and hand-object […]
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Pose-Guided Hierarchical Graph Reasoning for 3-D Hand Pose Estimation From a Single Depth Image
Paper Due to the self-similarity of the fingers and severe self-occlusion, it is difficult to predict the correct joint position from local evidence (as shown in Fig. a). By incorporating context information (as shown in Fig. b, where adjacent joints can be accurately predicted), forming enhanced feature maps through pose-guided hierarchical graph (PHG), ambiguity is […]
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Spatial-Aware Stacked Regression Network for Real-Time 3D Hand Pose Estimation
Paper Refer to SRN for more details. Bibtex
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AWR: Adaptive Weighting Regression for 3D Hand Pose Estimation
1State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, 2EBUPT Information Technology Co., Ltd. Paper Code We introduce adaptive weighting regression (AWR) method. The weight distribution in weight maps can be adjusted adaptively to achieve more accurate and robust performance under the guidance of joint supervision. Top row: When the […]
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SRN: Stacked Regression Network for Real-time 3D Hand Pose Estimation
Paper Code Normal hand Small hand Demos above are realtime results from Kinect V2 using models trained on Hands17 dataset (Intel Realsense SR300). Abstract Recently, most of state-of-the-art methods are based on 3D input data, because 3D data capture more spatial information than the depth image. However, these methods either require a complex network structure or time-consuming […]