用可变形3D线条从视频中抽象动态物体运动，实现直观的三维运动解析。
Recovering Dynamic 3D Sketches from Videos
- 基于预设模板生成可变形3D曲线，捕捉空间平滑的运动元素。
- 从视频帧提取3D点云运动引导，通过曲线变形抽象关键运动特征。
- 适用于未知结构物体，对环境干扰有强鲁棒性，适合动画与动作分析。
从视频中理解三维运动面临诸多挑战，因运动形式多样，涵盖刚体、可变形物体及关节结构。为此，我们提出Liv3Stroke，一种利用可变形3D线条抽象运动物体的新方法。物体的详细运动可通过无结构运动向量或一组运动基元（基于模板模型预定义的关节结构）表示。如同自由手绘线条可直观表达场景或意图，我们采用一组参数化3D曲线，捕捉一般物体中空间平滑的运动成分。首先，利用语义特征从视频帧中提取噪声较大的3D点云运动引导；随后，我们的方法通过变形一组曲线，将关键运动特征抽象为显式的3D表示。这种抽象方式在保持对环境因素鲁棒性的同时，实现了对运动主要成分的理解。该方法可直接从视频中分析3D物体运动，有效应对真实运动转录为影像时通常存在的不确定性。项目页面见：https://jaeah.me/liv3stroke_web
原文摘要 · Abstract (English)
Understanding 3D motion from videos presents inherent challenges due to the diverse types of movement, ranging from rigid and deformable objects to articulated structures. To overcome this, we propose Liv3Stroke, a novel approach for abstracting objects in motion with deformable 3D strokes. The detailed movements of an object may be represented by unstructured motion vectors or a set of motion primitives using a pre-defined articulation from a template model. Just as a free-hand sketch can intuitively visualize scenes or intentions with a sparse set of lines, we utilize a set of parametric 3D curves to capture a set of spatially smooth motion elements for general objects with unknown structures. We first extract noisy, 3D point cloud motion guidance from video frames using semantic features, and our approach deforms a set of curves to abstract essential motion features as a set of explicit 3D representations. Such abstraction enables an understanding of prominent components of motions while maintaining robustness to environmental factors. Our approach allows direct analysis of 3D object movements from video, tackling the uncertainty that typically occurs when translating real-world motion into recorded footage. The project page is accessible via: https://jaeah.me/liv3stroke_web
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