Efficient, Self-Supervised Human Pose Estimation with Inductive Prior Tuning

• URL: http://arxiv.org/abs/2311.02815v1
• Date: Mon, 6 Nov 2023 01:19:57 GMT
• Title: Efficient, Self-Supervised Human Pose Estimation with Inductive Prior Tuning
• Authors: Nobline Yoo, Olga Russakovsky
• Abstract summary: We analyze the relationship between reconstruction quality and pose estimation accuracy. We develop a model pipeline that outperforms the baseline, using less than one-third the amount of training data. We show that a combination of well-engineered reconstruction losses and inductive priors can help coordinate pose learning alongside reconstruction.
• Score: 30.256493625913127
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The goal of 2D human pose estimation (HPE) is to localize anatomical landmarks, given an image of a person in a pose. SOTA techniques make use of thousands of labeled figures (finetuning transformers or training deep CNNs), acquired using labor-intensive crowdsourcing. On the other hand, self-supervised methods re-frame the HPE task as a reconstruction problem, enabling them to leverage the vast amount of unlabeled visual data, though at the present cost of accuracy. In this work, we explore ways to improve self-supervised HPE. We (1) analyze the relationship between reconstruction quality and pose estimation accuracy, (2) develop a model pipeline that outperforms the baseline which inspired our work, using less than one-third the amount of training data, and (3) offer a new metric suitable for self-supervised settings that measures the consistency of predicted body part length proportions. We show that a combination of well-engineered reconstruction losses and inductive priors can help coordinate pose learning alongside reconstruction in a self-supervised paradigm.

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