HiPART：用于遮挡三维人体姿态估计的分层姿态自回归Transformer
HiPART: Hierarchical Pose AutoRegressive Transformer for Occluded 3D Human Pose Estimation
AI总结：
针对二维到三维人体姿态估计中遮挡问题，提出HiPART，通过两阶段生成式致密化生成分层二维密集姿态，提升鲁棒性，在单帧任务上达最优，并优于多帧方法且降低复杂度。
摘要：
现有的二维到三维人体姿态估计（HPE）方法通过在提升阶段丰富时间线索和视觉线索等信息来应对遮挡问题。本文指出，这些方法忽略了稀疏骨架二维输入表示的局限性，该局限性从根本上限制了二维到三维的提升过程，并加剧了遮挡问题。为解决这些问题，我们提出了一种新颖的两阶段生成式致密化方法，名为分层姿态自回归Transformer（HiPART），用于从原始稀疏二维姿态生成分层二维密集姿态。具体而言，我们首先开发了一个多尺度骨架分词模块，将高度密集的二维姿态量化为分层令牌，并提出了一种骨架感知对齐机制以增强令牌间的连接。随后，我们开发了一种分层自回归建模方案，用于分层二维姿态的生成。以生成的分层姿态作为二维到三维提升的输入，所提方法在遮挡场景中展现出强大的鲁棒性，并在基于单帧的三维人体姿态估计上取得了最先进的性能。此外，它在降低参数和计算复杂度的同时，优于众多多帧方法，并且可以补充这些方法以进一步提升性能和鲁棒性。
英文摘要：
Existing 2D-to-3D human pose estimation (HPE) methods struggle with the occlusion issue by enriching information like temporal and visual cues in the lifting stage. In this paper, we argue that these methods ignore the limitation of the sparse skeleton 2D input representation, which fundamentally restricts the 2D-to-3D lifting and worsens the occlusion issue. To address these, we propose a novel two-stage generative densification method, named Hierarchical Pose AutoRegressive Transformer (HiPART), to generate hierarchical 2D dense poses from the original sparse 2D pose. Specifically, we first develop a multi-scale skeleton tokenization module to quantize the highly dense 2D pose into hierarchical tokens and propose a Skeleton-aware Alignment to strengthen token connections. We then develop a Hierarchical AutoRegressive Modeling scheme for hierarchical 2D pose generation. With generated hierarchical poses as inputs for 2D-to-3D lifting, the proposed method shows strong robustness in occluded scenarios and achieves state-of-the-art performance on the single-frame-based 3D HPE. Moreover, it outperforms numerous multi-frame methods while reducing parameter and computational complexity and can also complement them to further enhance performance and robustness.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
