AI 中文总结
本文提出基于罗德里格斯向量空间拉普拉斯优化的运动编辑框架，可实现平滑运动拼接，支持跨类别替换，无需学习模型，效果优于线性插值，适用于动画创作与运动分析。
AI 中文摘要
本文提出了一种用于平滑运动拼接的运动编辑框架，该框架基于罗德里格斯向量（Rodrigues vector）空间中的拉普拉斯优化。通过将关节旋转表示为连续的罗德里格斯向量，运动拼接被构建为一个时域拉普拉斯优化问题，能够在保留参考运动的特征时域变化的同时，实现运动片段之间的平滑过渡。所提方法支持同类别内替换和跨类别运动拼接，无需依赖基于学习的模型或复杂的手动调参，且计算效率高，适用于交互式编辑。通过一系列拼接实验并与线性插值对比，我们证明拉普拉斯编辑在广泛的运动差异下能产生稳定且视觉连贯的过渡。此外，对旋转连续性的分析表明，实际运动数据中旋转轴反转的情况很少见，并解释了合成轴翻转场景中观察到的数值不稳定性为何不会出现在实际运动拼接中。这些结果凸显了旋转表示在稳定时域优化中的重要性，表明所提框架不仅适用于动画创作，还适用于运动分析及未来结合感知或生理线索的扩展应用。
英文摘要
This paper presents a motion editing framework for smooth motion stitching based on Laplacian optimization in Rodrigues vector space. By representing joint rotations as continuous Rodrigues vectors, motion stitching is formulated as a temporal Laplacian optimization problem, enabling smooth transitions between motion segments while preserving characteristic temporal variations of reference motions. The proposed approach supports both intra-category replacement and cross-category motion stitching without relying on learning-based models or complex manual tuning, and is computationally efficient for interactive editing. Through a series of stitching experiments and comparisons with linear interpolation, we demonstrate that Laplacian editing produces stable and visually coherent transitions under a wide range of motion differences. Furthermore, an analysis of rotational continuity clarifies that rotation-axis inversions are rare in real motion data and explains why numerical instabilities observed in synthetic axis-flipping scenarios do not arise in practical motion stitching. These results highlight the importance of rotational representation in stabilizing temporal optimization and suggest that the proposed framework is well suited not only for animation authoring but also for motion analysis and future extensions incorporating perceptual or physiological cues.
Comments6 pages, 8 figures. Presented at NICOGRAPH International 2026
DOI：10.23919/NICOIntCPS00076.2026.00012
