  There has been great progress in human 3D mesh recovery and great interest in
learning about the world from consumer video data. Unfortunately current
methods for 3D human mesh recovery work rather poorly on consumer video data,
since on the Internet, unusual camera viewpoints and aggressive truncations are
the norm rather than a rarity. We study this problem and make a number of
contributions to address it: (i) we propose a simple but highly effective
self-training framework that adapts human 3D mesh recovery systems to consumer
videos and demonstrate its application to two recent systems; (ii) we introduce
evaluation protocols and keypoint annotations for 13K frames across four
consumer video datasets for studying this task, including evaluations on
out-of-image keypoints; and (iii) we show that our method substantially
improves PCK and human-subject judgments compared to baselines, both on test
videos from the dataset it was trained on, as well as on three other datasets
without further adaptation. Project website:
https://crockwell.github.io/partial_humans
