- Greg Mori
- Caroline Pantofaru
- Nisarg Kothari
- Thomas Leung
- George Toderici
- Alexander Toshev
- Weilong Yang
arXiv (2015)
We present a method for learning an embedding that places images of humans in similar poses nearby. This embedding can be used as a direct method of comparing images based on human pose, avoiding potential challenges of estimating body joint positions. Pose embedding learning is formulated under a triplet-based distance criterion. A deep architecture is used to allow learning of a representation capable of making distinctions between different poses. Experiments on human pose matching and retrieval from video data demonstrate the potential of the method.
We maintain a portfolio of research projects, providing individuals and teams the freedom to emphasize specific types of work