  Recognition of human poses and actions is crucial for autonomous systems to
interact smoothly with people. However, cameras generally capture human poses
in 2D as images and videos, which can have significant appearance variations
across viewpoints that make the recognition tasks challenging. To address this,
we explore recognizing similarity in 3D human body poses from 2D information,
which has not been well-studied in existing works. Here, we propose an approach
to learning a compact view-invariant embedding space from 2D body joint
keypoints, without explicitly predicting 3D poses. Input ambiguities of 2D
poses from projection and occlusion are difficult to represent through a
deterministic mapping, and therefore we adopt a probabilistic formulation for
our embedding space. Experimental results show that our embedding model
achieves higher accuracy when retrieving similar poses across different camera
views, in comparison with 3D pose estimation models. We also show that by
training a simple temporal embedding model, we achieve superior performance on
pose sequence retrieval and largely reduce the embedding dimension from
stacking frame-based embeddings for efficient large-scale retrieval.
Furthermore, in order to enable our embeddings to work with partially visible
input, we further investigate different keypoint occlusion augmentation
strategies during training. We demonstrate that these occlusion augmentations
significantly improve retrieval performance on partial 2D input poses. Results
on action recognition and video alignment demonstrate that using our embeddings
without any additional training achieves competitive performance relative to
other models specifically trained for each task.
