DeProPose: Deficiency-Proof 3D Human Pose Estimation via Adaptive Multi-View Fusion

• URL: http://arxiv.org/abs/2502.16419v1
• Date: Sun, 23 Feb 2025 03:22:54 GMT
• Title: DeProPose: Deficiency-Proof 3D Human Pose Estimation via Adaptive Multi-View Fusion
• Authors: Jianbin Jiao, Xina Cheng, Kailun Yang, Xiangrong Zhang, Licheng Jiao,
• Abstract summary: We introduce the task of Deficiency-Aware 3D Pose Estimation.<n>DeProPose is a flexible method that simplifies the network architecture to reduce training complexity.<n>We have developed a novel 3D human pose estimation dataset.
• Score: 57.83515140886807
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 3D human pose estimation has wide applications in fields such as intelligent surveillance, motion capture, and virtual reality. However, in real-world scenarios, issues such as occlusion, noise interference, and missing viewpoints can severely affect pose estimation. To address these challenges, we introduce the task of Deficiency-Aware 3D Pose Estimation. Traditional 3D pose estimation methods often rely on multi-stage networks and modular combinations, which can lead to cumulative errors and increased training complexity, making them unable to effectively address deficiency-aware estimation. To this end, we propose DeProPose, a flexible method that simplifies the network architecture to reduce training complexity and avoid information loss in multi-stage designs. Additionally, the model innovatively introduces a multi-view feature fusion mechanism based on relative projection error, which effectively utilizes information from multiple viewpoints and dynamically assigns weights, enabling efficient integration and enhanced robustness to overcome deficiency-aware 3D Pose Estimation challenges. Furthermore, to thoroughly evaluate this end-to-end multi-view 3D human pose estimation model and to advance research on occlusion-related challenges, we have developed a novel 3D human pose estimation dataset, termed the Deficiency-Aware 3D Pose Estimation (DA-3DPE) dataset. This dataset encompasses a wide range of deficiency scenarios, including noise interference, missing viewpoints, and occlusion challenges. Compared to state-of-the-art methods, DeProPose not only excels in addressing the deficiency-aware problem but also shows improvement in conventional scenarios, providing a powerful and user-friendly solution for 3D human pose estimation. The source code will be available at https://github.com/WUJINHUAN/DeProPose.

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