  Depictions of similar human body configurations can vary with changing
viewpoints. Using only 2D information, we would like to enable vision
algorithms to recognize similarity in human body poses across multiple views.
This ability is useful for analyzing body movements and human behaviors in
images and videos. In this paper, we propose an approach for learning a compact
view-invariant embedding space from 2D joint keypoints alone, without
explicitly predicting 3D poses. Since 2D poses are projected from 3D space,
they have an inherent ambiguity, which is difficult to represent through a
deterministic mapping. Hence, we use probabilistic embeddings to model this
input uncertainty. Experimental results show that our embedding model achieves
higher accuracy when retrieving similar poses across different camera views, in
comparison with 2D-to-3D pose lifting models. We also demonstrate the
effectiveness of applying our embeddings to view-invariant action recognition
and video alignment. Our code is available at
https://github.com/google-research/google-research/tree/master/poem.
