Get pose in multi-person motion with occlusion by using identify and trajectory with D-MAE
Get pose in multi-person motion with occlusion by using identify and trajectory with D-MAE
A Dual-Masked Auto-Encoder for Robust Motion Capture with Spatial-Temporal Skeletal Token Completion
arXiv paper abstract https://arxiv.org/abs/2207.07381v1
arXiv PDF paper https://arxiv.org/pdf/2207.07381v1.pdf
Multi-person motion capture can be challenging due to ambiguities caused by severe occlusion, fast body movement, and complex interactions.
Existing frameworks build on 2D pose estimations and triangulate to 3D coordinates via reasoning the appearance, trajectory, and geometric consistencies among multi-camera observations.
However, 2D joint detection is usually incomplete and with wrong identity assignments due to limited observation angle, which leads to noisy 3D triangulation results.
… propose an adaptive, identity-aware triangulation module to reconstruct 3D joints and identify the missing joints for each identity.
… then propose a Dual-Masked Auto-Encoder (D-MAE) which encodes the joint status with both skeletal-structural and temporal position encoding for trajectory completion.
… demonstrate the efficiency of … proposed model, as well as its advantage against the other state-of-the-art methods.
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