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arXiv · 2107.02133

Test-Time Personalization with a Transformer for Human Pose Estimation

Yizhuo Li·Miao Hao·Zonglin Di·Nitesh B. Gundavarapu·Xiaolong Wang
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Abstract

We propose to personalize a human pose estimator given a set of test images of a person without using any manual annotations. While there is a significant advancement in human pose estimation, it is still very challenging for a model to generalize to different unknown environments and unseen persons. Instead of using a fixed model for every test case, we adapt our pose estimator during test time to exploit person-specific information. We first train our model on diverse data with both a supervised and a self-supervised pose estimation objectives jointly. We use a Transformer model to build a transformation between the self-supervised keypoints and the supervised keypoints. During test time, we personalize and adapt our model by fine-tuning with the self-supervised objective. The pose is then improved by transforming the updated self-supervised keypoints. We experiment with multiple datasets and show significant improvements on pose estimations with our self-supervised personalization.

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Yizhuo Li, Miao Hao, Zonglin Di, Nitesh B. Gundavarapu, Xiaolong Wang. 2021-07-05. Test-Time Personalization with a Transformer for Human Pose Estimation. https://arxiv.org/abs/2107.02133

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