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AI Learns Human Pose Estimation From Videos

AI Learns Human Pose Estimation From Videos

 

 

We involve human annotators to establish dense correspondences from 2D images to surface-based representations of the human body. If done naively, this would require by manipulating a surface through rotations – which can be frustratingly inefficient. Instead, we construct a two-stage annotation pipeline to efficiently gather annotations for image-to-surface correspondence.

As shown below, in the first stage we ask annotators to delineate regions corresponding to visible, semantically defined body parts. We instruct the annotators to estimate the body part behind the clothes, so that for instance wearing a large skirt would not complicate the subsequent annotation of correspondences.

In the second stage we sample every part region with a set of roughly equidistant points and request the annotators to bring these points in correspondence with the surface. In order to simplify this task we `unfold’ the part surface by providing six pre-rendered views of the same body part and allow the user to place landmarks on any of them. This allows the annotator to choose the most convenient point of view by selecting one among six options instead of manually rotating the surface.
We use the SMPL model and SURREAL textures in the data gathering procedure.

The two-stage annotation process has allowed us to very efficiently gather highly accurate correspondences. We have seen that the part segmentation and correspondence annotation tasks take ap- proximately the same time, which is surprising given the more challenging nature of the latter task. We have gathered annotations for 50K humans, collecting more then 5 million manually annotated correspondences. Below are visualizations of annotations on images from our validation set: Image (left), U (middle) and V (right) values for the collected points.

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