Abstract
As an important computer vision task, 3D human pose estimation has received widespread attention and many applications have been derived from it. Most previous methods address this task by using a 3D pictorial structure model which is inefficient due to the huge state space. We propose a novel approach to solve this problem. Our key idea is to learn confidence weights of each joint from the input image through a simple neutral network. We also extract the confidence matrix of heatmaps which reflects its feature quality in order to enhance the feature quality in occluded views. Our approach is end-to-end differentiable which can improve the efficiency and robustness. We evaluate the approach on two public datasets including Human3.6M and Occlusion-Person which achieves significant performance gains compare with the state-of-the-art.
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Ge, S., Yu, H., Zhang, Y., Shi, H., Gao, H. (2022). 3D Human Pose Estimation Based on Multi-feature Extraction. In: Fang, L., Povey, D., Zhai, G., Mei, T., Wang, R. (eds) Artificial Intelligence. CICAI 2022. Lecture Notes in Computer Science(), vol 13606. Springer, Cham. https://doi.org/10.1007/978-3-031-20503-3_51
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