The invention relates to a geometric driving-based self-supervised human body 3D attitude estimation network training method, belongs to the field of artificial intelligence computer vision, and can effectively solve the technical problem that the current human body 3D attitude network training based on deep learning needs a large amount of human body 3D articulation point labeling data. The method does not depend on human body 3D joint point labeling data, completely depends on geometric priori knowledge, and can avoid the tedious human body 3D articulation point labeling process; the proposed transformation reprojection loss can explore the multi-view consistency to train a human body 3D attitude estimation network. A more accurate human body 3D posture prediction result can be obtainedin the training process; according to the proposed root node position estimation network, the scale information of the reprojected 2D attitude is reserved in the 3D attitude estimation network training process, a more accurate human body 3D attitude prediction result can be obtained in the training process, and the proposed network pre-training method can help network training to effectively converge.
Geometric driving-based self-supervised human body 3D attitude estimation network training method