Full-Body Awareness from Partial Observations

• URL: http://arxiv.org/abs/2008.06046v1
• Date: Thu, 13 Aug 2020 17:59:11 GMT
• Title: Full-Body Awareness from Partial Observations
• Authors: Chris Rockwell, David F. Fouhey
• Abstract summary: We propose a self-training framework that adapts human 3D mesh recovery systems to consumer videos. We show that our method substantially improves PCK and human-subject judgments compared to baselines.
• Score: 17.15829643665034
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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

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