EgoPoseFormer: A Simple Baseline for Stereo Egocentric 3D Human Pose Estimation

• URL: http://arxiv.org/abs/2403.18080v2
• Date: Thu, 15 Aug 2024 17:08:49 GMT
• Title: EgoPoseFormer: A Simple Baseline for Stereo Egocentric 3D Human Pose Estimation
• Authors: Chenhongyi Yang, Anastasia Tkach, Shreyas Hampali, Linguang Zhang, Elliot J. Crowley, Cem Keskin,
• Abstract summary: We present EgoPoseFormer, a transformer-based model for stereo egocentric human pose estimation. Our approach overcomes the main challenge of overcoming joint invisibility caused by self-occlusion or a limited field of view (FOV) of head-mounted cameras. We evaluate our method on the stereo UnrealEgo dataset and show it significantly outperforms previous approaches.
• Score: 15.590340765703893
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We present EgoPoseFormer, a simple yet effective transformer-based model for stereo egocentric human pose estimation. The main challenge in egocentric pose estimation is overcoming joint invisibility, which is caused by self-occlusion or a limited field of view (FOV) of head-mounted cameras. Our approach overcomes this challenge by incorporating a two-stage pose estimation paradigm: in the first stage, our model leverages the global information to estimate each joint's coarse location, then in the second stage, it employs a DETR style transformer to refine the coarse locations by exploiting fine-grained stereo visual features. In addition, we present a Deformable Stereo Attention operation to enable our transformer to effectively process multi-view features, which enables it to accurately localize each joint in the 3D world. We evaluate our method on the stereo UnrealEgo dataset and show it significantly outperforms previous approaches while being computationally efficient: it improves MPJPE by 27.4mm (45% improvement) with only 7.9% model parameters and 13.1% FLOPs compared to the state-of-the-art. Surprisingly, with proper training settings, we find that even our first-stage pose proposal network can achieve superior performance compared to previous arts. We also show that our method can be seamlessly extended to monocular settings, which achieves state-of-the-art performance on the SceneEgo dataset, improving MPJPE by 25.5mm (21% improvement) compared to the best existing method with only 60.7% model parameters and 36.4% FLOPs. Code is available at: https://github.com/ChenhongyiYang/egoposeformer .

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