Concetti Chiave
Proposing a novel biomechanics-aware network for accurate 3D kinematics estimation using synthetic data, outperforming existing methods.
Sintesi
Accurate 3D kinematics estimation is crucial for various applications in human health and mobility.
Conventional marker-based motion capture is expensive and limited by expertise and datasets.
Proposed biomechanics-aware network directly outputs 3D kinematics from two input views.
Synthetic dataset ODAH is created for training with accurate kinematics annotations.
Extensive experiments show the proposed approach outperforms state-of-the-art methods.
Contributions include an end-to-end 3D kinematics estimation model and a synthetic video dataset.
The proposed method demonstrates strong generalization across multiple datasets.
Statistiche
"Our extensive experiments demonstrate that the proposed approach, only trained on synthetic data, outperforms previous state-of-the-art methods when evaluated across multiple datasets."
"ODAH has 1132 videos in 60 fps, and each video has a duration of around 10 seconds."
"The proposed biomechanics-aware network achieves superior performance in average joint angle error and joint position error across all datasets."
Citazioni
"Accurate 3D kinematics estimation of human body is crucial in various applications for human health and mobility."
"Our proposed method achieves the best performances in terms of averaged joint angle and keypoint position errors."