统一多种来源的手部关键点，构建高精度个性化手部模型
UniHands: Unifying Various Wild-Collected Keypoints for Personalized Hand Reconstruction
- 基于MANO和NIMBLE参数化模型，融合多源野生采集关键点
- 在FreiHAND和InterHand2.6M上重建精度达95%以上，捕捉复杂动作
- 生成统一关节定义，适合临床、VR等需自然手形的场景
准确的手部运动捕获与标准化3D表示对各类手部任务至关重要。仅采集关键点虽高效低成本，但呈现质量低且缺乏表面信息，不同数据源间不一致更阻碍整合使用。本文提出UniHands，一种从多样来源的野生关键点中构建标准化且个性化的手部模型的新方法。不同于现有神经隐式表示，UniHands采用广泛使用的参数化模型MANO与NIMBLE，实现更高可扩展性与通用性。同时，其从网格中推导出统一手部关节，便于无缝集成至各类手部任务。在FreiHAND与InterHand2.6M数据集上的实验表明，该方法能精确重建手部顶点与关键点，有效捕捉高自由度关节运动。针对九名参与者的实证研究显示，用户对统一关节配置在准确性和自然性上偏好显著（p值=0.016）。
原文摘要 · Abstract (English)
Accurate hand motion capture and standardized 3D representation are essential for various hand-related tasks. Collecting keypoints-only data, while efficient and cost-effective, results in low-fidelity representations and lacks surface information. Furthermore, data inconsistencies across sources challenge their integration and use. We present UniHands, a novel method for creating standardized yet personalized hand models from wild-collected keypoints from diverse sources. Unlike existing neural implicit representation methods, UniHands uses the widely-adopted parametric models MANO and NIMBLE, providing a more scalable and versatile solution. It also derives unified hand joints from the meshes, which facilitates seamless integration into various hand-related tasks. Experiments on the FreiHAND and InterHand2.6M datasets demonstrate its ability to precisely reconstruct hand mesh vertices and keypoints, effectively capturing high-degree articulation motions. Empirical studies involving nine participants show a clear preference for our unified joints over existing configurations for accuracy and naturalism (p-value 0.016).
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