HandMvNet: Real-Time 3D Hand Pose Estimation Using Multi-View Cross-Attention Fusion
In this work, we present HandMvNet, one of the first real-time method designed to estimate 3D hand motion and shape from multi-view camera images. Unlike previous monocular approaches, which suffer from scale-depth ambiguities, our method ensures consistent and accurate absolute hand poses and shapes. This is achieved through a multi-view attention-fusion mechanism that effectively integrates features from multiple viewpoints. In contrast to previous multi-view methods, our approach eliminates the need for camera parameters as input to learn 3D geometry. HandMvNet also achieves a substantial r
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- FuzzyOverlapping authors or contributors · 62%ultralytics/yolov5 →
“Shared author/contributor keys: ali”
- LinkedLinked via arxiv author · 85%Muhammad Asad Ali →
“HandMvNet: Real-Time 3D Hand Pose Estimation Using Multi-View Cross-Attention Fusion”
- LinkedLinked via arxiv author · 85%Nadia Robertini →
“HandMvNet: Real-Time 3D Hand Pose Estimation Using Multi-View Cross-Attention Fusion”
- LinkedLinked via arxiv author · 85%Didier Stricker →
“HandMvNet: Real-Time 3D Hand Pose Estimation Using Multi-View Cross-Attention Fusion”