Adversarial Motion Modelling helps Semi-supervised Hand Pose Estimation

• URL: http://arxiv.org/abs/2106.05954v1
• Date: Thu, 10 Jun 2021 17:50:19 GMT
• Title: Adversarial Motion Modelling helps Semi-supervised Hand Pose Estimation
• Authors: Adrian Spurr, Pavlo Molchanov, Umar Iqbal, Jan Kautz, Otmar Hilliges
• Abstract summary: We propose to combine ideas from adversarial training and motion modelling to tap into unlabeled videos. We show that an adversarial leads to better properties of the hand pose estimator via semi-supervised training on unlabeled video sequences. The main advantage of our approach is that we can make use of unpaired videos and joint sequence data both of which are much easier to attain than paired training data.
• Score: 116.07661813869196
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Hand pose estimation is difficult due to different environmental conditions, object- and self-occlusion as well as diversity in hand shape and appearance. Exhaustively covering this wide range of factors in fully annotated datasets has remained impractical, posing significant challenges for generalization of supervised methods. Embracing this challenge, we propose to combine ideas from adversarial training and motion modelling to tap into unlabeled videos. To this end we propose what to the best of our knowledge is the first motion model for hands and show that an adversarial formulation leads to better generalization properties of the hand pose estimator via semi-supervised training on unlabeled video sequences. In this setting, the pose predictor must produce a valid sequence of hand poses, as determined by a discriminative adversary. This adversary reasons both on the structural as well as temporal domain, effectively exploiting the spatio-temporal structure in the task. The main advantage of our approach is that we can make use of unpaired videos and joint sequence data both of which are much easier to attain than paired training data. We perform extensive evaluation, investigating essential components needed for the proposed framework and empirically demonstrate in two challenging settings that the proposed approach leads to significant improvements in pose estimation accuracy. In the lowest label setting, we attain an improvement of $40\%$ in absolute mean joint error.

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