Problem
Research questions and friction points this paper is trying to address.
Optical Motion Capture
Unconstrained Markers
Parametric Human Models
DirtyMoCap: Robust Motion Capture from Unconstrained Markers
🤖 AI Summary
为解决光学动作捕捉中无约束标记点带来的问题，本文提出DirtyMoCap框架，通过将无序标记映射到固定代理锚点并使用可微高斯-牛顿求解器拟合SMPL-H模型来恢复人体姿态。
📝 Abstract
Optical motion capture delivers high-fidelity human motion, but its reliance on strict marker layouts and clean trajectories severely limits its real-world applicability. In practice, tracking systems frequently output unconstrained markers: sparse, noisy, and unordered point clouds with unknown or varying configurations. To bridge the gap between corrupted raw markers and parametric human models, we introduce DirtyMoCap, a robust, marker-layout-free framework. Our core insight is to map unordered marker observations to a fixed set of "proxy anchors" comprising skeletal joints and body surface points, which serve as a stable intermediate representation. We first initialize and track these anchors over long sequences using a recurrent sliding-window architecture. Then, a custom differentiable Gauss-Newton solver fits the SMPL-H model to the tracked anchors to recover full-body pose, translation, and shape. By explicitly deriving geometric residuals, our solver learns adaptive observation confidence, smoothness, and prior weights end-to-end, adapting dynamically to the reliability of the input data. Extensive experiments on diverse, noisy marker configurations demonstrate that DirtyMoCap successfully generalizes across arbitrary layouts using only a single trained model. It consistently outperforms state-of-the-art configuration-specific baselines in both joint and vertex reconstruction accuracy, while our custom CUDA solver achieves up to a 100x speedup over standard PyTorch implementations. We further apply DirtyMoCap to heterogeneous raw optical MoCap recordings of traditional Chinese martial arts, yielding a Kung Fu motion dataset of temporally coherent SMPL-H reconstructions. Code and data are available at https://wanglongzju.github.io/DirtyMoCap-Project-Page.
Innovation
Methods, ideas, or system contributions that make the work stand out.
DirtyMoCap
marker-layout-free
proxy anchors
differentiable Gauss-Newton solver
end-to-end learning
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