  Estimating abnormal posture based on 3D pose is vital in human pose analysis,
yet it presents challenges, especially when reconstructing 3D human poses from
monocular datasets with occlusions. Accurate reconstructions enable the
restoration of 3D movements, which assist in the extraction of semantic details
necessary for analyzing abnormal behaviors. However, most existing methods
depend on predefined key points as a basis for estimating the coordinates of
occluded joints, where variations in data quality have adversely affected the
performance of these models. In this paper, we present OAD2D, which
discriminates against motion abnormalities based on reconstructing 3D
coordinates of mesh vertices and human joints from monocular videos. The OAD2D
employs optical flow to capture motion prior information in video streams,
enriching the information on occluded human movements and ensuring
temporal-spatial alignment of poses. Moreover, we reformulate the abnormal
posture estimation by coupling it with Motion to Text (M2T) model in which, the
VQVAE is employed to quantize motion features. This approach maps motion tokens
to text tokens, allowing for a semantically interpretable analysis of motion,
and enhancing the generalization of abnormal posture detection boosted by
Language model. Our approach demonstrates the robustness of abnormal behavior
detection against severe and self-occlusions, as it reconstructs human motion
trajectories in global coordinates to effectively mitigate occlusion issues.
Our method, validated using the Human3.6M, 3DPW, and NTU RGB+D datasets,
achieves a high $F_1-$Score of 0.94 on the NTU RGB+D dataset for medical
condition detection. And we will release all of our code and data.
