#dense pose

DensePose goes beyond basic object recognition. Besides detecting humans in pictures, it can also make 3D models of their bodies by estimating the positions of their torsos and limbs. Those models can then enable the technology to create real-time 3D recreations of human movement in 2D videos. For example, it could produce videos that show models of several people kicking soccer balls or a single individual riding on a motorcycle.

This work could prove useful for "graphics, augmented reality, or human-computer interaction, and could also be a stepping stone towards general 3D-based object understanding," according to the Facebook AI Research (FAIR) paper published in January 2018.

But there is a "troubling implication of this research" that could enable "real-time surveillance," said Jack Clark, strategy and communications director at OpenAI, a nonprofit AI research company, in his popular newsletter called Import AI. Clark first discussed the implications of Facebook's DensePose paper in the February issue of his newsletter, and followed up in June after Facebook released the DensePose code on the software development platform Github.

"The same system has wide utility within surveillance architectures, potentially letting operators analyze large groups of people to work out if their movements are problematic or not—for instance, such a system could be used to signal to another system if a certain combination of movements are automatically labelled as portending a protest or a riot," Clark wrote in his newsletter.

In fact, other research groups have been working on similar systems to estimate human body poses for security applications: a group of UK and Indian researchers have been developing a drone-mounted system aimed at detecting violence within crowds of people. And there are clearly law enforcement agencies and governments around the world interested in potentially harnessing such technology, for good or for ill.