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3D HUMAN BODY POST ESTIMATE USING A MODEL TRAINED FROM UNLABELED MULTI-VIEW DATA
3D HUMAN BODY POST ESTIMATE USING A MODEL TRAINED FROM UNLABELED MULTI-VIEW DATA
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机译：3D使用从未标记的多视图数据训练的模型的人体折叠估算
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摘要
Learning to estimate a 3D body pose, as well as the pose of any object from a single 2D image, is of great interest to many practical graphics applications and generally relies on neural networks trained with sample data that includes each 2D sample image Annotate (label) a known 3D pose. However, the requirement of this tagged training data has several disadvantages including, for example, traditionally used training data sets are not diverse enough and therefore limit the extent to which neural networks can estimate the 3-D pose. Extending these training data sets is also difficult, since it requires manually provided annotations for 2D images, which is time-consuming and error-prone. The invention overcomes these and other limitations of existing techniques by providing a model trained on unlabeled multi-view data for use in 3D pose estimation.
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