Thanks to the development of 2D keypoint detectors, monocular 3D human pose estimation (HPE) via 2D-to-3D uplifting approaches have achieved remarkable improvements. Still, monocular 3D HPE is a challenging problem due to the inherent depth ambiguities and occlusions. To handle this problem, many previous works exploit temporal information to mitigate such difficulties. However, there are many real-world applications where frame sequences are not accessible. This paper focuses on reconstructing a 3D pose from a single 2D keypoint detection. Rather than exploiting temporal information, we alleviate the depth ambiguity by generating multiple 3D pose candidates which can be mapped to an identical 2D keypoint. We build a novel diffusion-based framework to effectively sample diverse 3D poses from an off-the-shelf 2D detector. By considering the correlation between human joints by replacing the conventional denoising U-Net with graph convolutional network, our approach accomplishes further performance improvements. We evaluate our method on the widely adopted Human3.6M and HumanEva-I datasets. Comprehensive experiments are conducted to prove the efficacy of the proposed method, and they confirm that our model outperforms state-of-the-art multi-hypothesis 3D HPE methods.

翻译：得益于二维关键点检测器的发展，通过二维到三维升维方法实现的单目三维人体姿态估计已取得显著进步。然而，由于固有的深度模糊性和遮挡问题，单目三维人体姿态估计仍然是一项具有挑战性的课题。为解决该问题，许多先前工作利用时序信息来缓解此类困难。但现实中存在诸多无法获取帧序列的应用场景。本文聚焦于从单一二维关键点检测结果重建三维姿态。不同于利用时序信息的方法，我们通过生成可映射至同一二维关键点的多个三维姿态候选来缓解深度模糊性。我们构建了新颖的扩散框架，能够从现成的二维检测器中有效采样多样化的三维姿态。通过采用图卷积网络替代传统去噪U-Net以建模人体关节点间的相关性，所提方法实现了进一步性能提升。我们在广泛使用的Human3.6M和HumanEva-I数据集上评估了该方法，并通过全面实验证明了所提方法的有效性，结果表明本模型优于当前最先进的多假设三维人体姿态估计方法。