Another exciting application of computer vision in augmented reality is in the field of pose estimation-without which human expression through markerless augmented reality would be limited.ĢD pose estimation, which extracts human joint information across a flat plane (along the x and y axes), can be used for applications needing behavior recognition, gesture recognition, and abstracted human input into an environment. AR e-commerce and design experiences rely on this improved accuracy to render digital objects at scale and prototype placement. Techniques in the field of computer vision have enabled better surface detection and orientation, as well as depth perception for spawning digital objects with more accuracy. Other promising advancements in augmented reality have followed from markerless augmented reality, or the ability to map digital objects to an environment without prior information about-or physical markers and trackers within-that environment. Pokemon Go, the breakout AR success of 2016, leveraged location-based mobile AR to solve this problem, proving that AR could inspire a newfound engagement with the world given the right digital easter eggs, distributed just so. How do we get digital objects to play well with reality (and with the human at the center of an augmented experience)? Creating compelling AR experiences hangs in the balance of this question and the way we integrate virtual overlays with real world environments.

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