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Grid Convolution for 3D Human Pose Estimation
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AI
DOI：10.1109/TPAMI.2025.3632829.png)
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3D human pose estimation from 2D keypoint observation has been used in many human-centered computer vision applications. In this work, we tackle the task by formulating a novel grid representation learning paradigm that relies on grid convolution (GridConv), mimicking the wisdom of regular convolution operations in image space. GridConv is defined based on Semantic Grid Transformation (SGT) which leverages a binary assignment matrix to map standard skeleton 2D pose onto a regular weave-like grid pose joint by joint. We provide two ways to implement SGT: handcrafted and learnable SGT. Surprisingly, both designs turn out to achieve promising results and the learnable one is better, demonstrating the great potential of this new lifting representation learning formulation. To improve the ability of GridConv to encode contextual cues, we introduce an attention module over the convolutional kernel, making grid convolution operations input-dependent, spatial-aware and grid-specific. Besides our spatial grid lifting network for single-frame input, we also present a spatial-temporal grid lifting network for video-based input, which relies on an efficient multi-scale grid learning strategy to encode spatial and temporal joint variations. Extensive experiments demonstrate that the proposed grid lifting network outperforms existing approaches by remarkable margins on Human3.6M and MPI-INF-3DHP datasets. Our grid lifting networks also exhibit good generalization ability across three other keypoint-based tasks: 3D hand pose estimation, head pose estimation, and action recognition.
Keyword:
3D human pose estimation
2D-to-3D pose lifting
grid convolution
graph convolutional network
期刊
IF:
18.6
论文数:
1.1K
被引数:
9.8W
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引用论文
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Pose-Oriented Transformer with Uncertainty-Guided Refinement for 2D-to-3D Human Pose Estimation面向姿态的变换器，具有不确定性引导的细化，用于2d到3d人体姿态估计
OAAI
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OAAI分享收藏Human3.6M: Large Scale Datasets and Predictive Methods for 3D Human Sensing in Natural EnvironmentsHuman3.6M: 自然环境中3D人体感知的大规模数据集和预测方法
PREAI
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