Building Spatio-temporal Transformers for Egocentric 3D Pose Estimation

• URL: http://arxiv.org/abs/2206.04785v1
• Date: Thu, 9 Jun 2022 22:33:27 GMT
• Title: Building Spatio-temporal Transformers for Egocentric 3D Pose Estimation
• Authors: Jinman Park, Kimathi Kaai, Saad Hossain, Norikatsu Sumi, Sirisha Rambhatla, Paul Fieguth
• Abstract summary: We leverage information from past frames to guide our self-attention-based 3D estimation procedure -- Ego-STAN. Specifically, we build atemporal Transformer model that attends to semantically rich convolutional neural network-based feature maps. We demonstrate Ego-STAN's superior performance on the xR-EgoPose dataset.
• Score: 9.569752078386006
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Egocentric 3D human pose estimation (HPE) from images is challenging due to severe self-occlusions and strong distortion introduced by the fish-eye view from the head mounted camera. Although existing works use intermediate heatmap-based representations to counter distortion with some success, addressing self-occlusion remains an open problem. In this work, we leverage information from past frames to guide our self-attention-based 3D HPE estimation procedure -- Ego-STAN. Specifically, we build a spatio-temporal Transformer model that attends to semantically rich convolutional neural network-based feature maps. We also propose feature map tokens: a new set of learnable parameters to attend to these feature maps. Finally, we demonstrate Ego-STAN's superior performance on the xR-EgoPose dataset where it achieves a 30.6% improvement on the overall mean per-joint position error, while leading to a 22% drop in parameters compared to the state-of-the-art.

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