Thin Slicing for Pose: Learning to Understand Pose without Explicit Pose Estimation
We address the problem of learning a pose aware, compact embedding that projects images with similar human poses to be placed close by in the embedding space. The embedding function is built on a deep convolutional network, and trained with triplet based rank constraints on real image data. This architecture allows us to learn a robust representation that captures differences in human poses by effectively factoring out variations in clothing, background, and imaging conditions in the wild. For a variety of pose related tasks, the proposed pose embedding provides a cost efficient and natural alternative to explicit pose estimation, circumventing challenges of localizing body joints. We demonstrate the efficacy of the embedding on pose based image retrieval and action recognition problems.
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)