提出新点云表征Sparkle，兼顾精度与鲁棒性。
Sparkle: A Robust and Versatile Representation for Point Cloud based Human Motion Capture
- 将骨骼结构与表面锚点统一建模，显式解耦运动与几何。
- 在噪声、遮挡下仍保持高精度，跨域泛化能力更强。
- 适合真实场景下的鲁棒人体动作捕捉应用。
基于点云的人体动作捕捉利用丰富的空间几何信息和隐私保护传感优势，但从噪声大、无序的点云中学习鲁棒表示仍是挑战。现有方法在基于点的方法（几何细节丰富但易受噪声影响）与基于骨架的方法（鲁棒但过于简化）之间难以平衡。本文提出Sparkle，一种统一骨骼关节与表面锚点的结构化表征，通过显式的运动-几何解耦实现高效建模。其框架SparkleMotion采用分层模块，嵌入几何连续性与运动约束。通过明确分离内部运动结构与外部表面几何，该方法在准确率、鲁棒性及域偏移、噪声、遮挡等极端条件下的泛化能力上均达到当前最优。大量实验证明其在多种传感器类型和复杂现实场景中表现卓越。
原文摘要 · Abstract (English)
Point cloud-based motion capture leverages rich spatial geometry and privacy-preserving sensing, but learning robust representations from noisy, unstructured point clouds remains challenging. Existing approaches face a struggle trade-off between point-based methods (geometrically detailed but noisy) and skeleton-based ones (robust but oversimplified). We address the fundamental challenge: how to construct an effective representation for human motion capture that can balance expressiveness and robustness. In this paper, we propose Sparkle, a structured representation unifying skeletal joints and surface anchors with explicit kinematic-geometric factorization. Our framework, SparkleMotion, learns this representation through hierarchical modules embedding geometric continuity and kinematic constraints. By explicitly disentangling internal kinematic structure from external surface geometry, SparkleMotion achieves state-of-the-art performance not only in accuracy but crucially in robustness and generalization under severe domain shifts, noise, and occlusion. Extensive experiments demonstrate our superiority across diverse sensor types and challenging real-world scenarios.
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