中文标题#
HyperDiff：超图引导的扩散模型用于 3D 人体姿态估计
英文标题#
HyperDiff: Hypergraph Guided Diffusion Model for 3D Human Pose Estimation
中文摘要#
单目 3D 人体姿态估计（HPE）在从 2D 到 3D 的提升过程中常常遇到深度模糊和遮挡等挑战。此外，传统方法在利用骨骼结构信息时可能忽略多尺度骨骼特征，这可能会对姿态估计的准确性产生负面影响。为了解决这些挑战，本文引入了一种新颖的 3D 姿态估计方法，HyperDiff，该方法将扩散模型与 HyperGCN 相结合。扩散模型有效地捕捉数据不确定性，缓解深度模糊和遮挡。同时，作为去噪器的 HyperGCN 采用多粒度结构，准确建模关节之间的高阶相关性。这提高了模型的去噪能力，特别是在复杂姿态的情况下。实验结果表明，HyperDiff 在 Human3.6M 和 MPI-INF-3DHP 数据集上达到了最先进的性能，并能灵活适应不同的计算资源，以平衡性能和效率。
英文摘要#
Monocular 3D human pose estimation (HPE) often encounters challenges such as depth ambiguity and occlusion during the 2D-to-3D lifting process. Additionally, traditional methods may overlook multi-scale skeleton features when utilizing skeleton structure information, which can negatively impact the accuracy of pose estimation. To address these challenges, this paper introduces a novel 3D pose estimation method, HyperDiff, which integrates diffusion models with HyperGCN. The diffusion model effectively captures data uncertainty, alleviating depth ambiguity and occlusion. Meanwhile, HyperGCN, serving as a denoiser, employs multi-granularity structures to accurately model high-order correlations between joints. This improves the model's denoising capability especially for complex poses. Experimental results demonstrate that HyperDiff achieves state-of-the-art performance on the Human3.6M and MPI-INF-3DHP datasets and can flexibly adapt to varying computational resources to balance performance and efficiency.
文章页面#
HyperDiff：超图引导的扩散模型用于 3D 人体姿态估计
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