Articulat3D: 从单目视频中利用几何和运动约束重建关节式数字孪生
Articulat3D: Reconstructing Articulated Digital Twins From Monocular Videos with Geometric and Motion Constraints
AI总结：
提出Articulat3D框架，通过运动先验初始化和几何运动约束优化，从单目视频重建高保真关节式数字孪生，在合成和真实数据上达到最优性能。
摘要：
从视觉数据构建关节式物体的高保真数字孪生仍然是一个核心挑战。现有方法依赖于物体在离散、静态状态下的多视角捕获，这严重限制了它们在现实世界中的可扩展性。在本文中，我们介绍Articulat3D，一种新颖的框架，通过联合施加显式的3D几何和运动约束，从随意捕获的单目视频中构建此类数字孪生。我们首先提出运动先验驱动初始化，利用3D点轨迹来挖掘关节式运动的低维结构。通过用一组紧凑的运动基对场景动态进行建模，我们促进了场景到多个刚性运动组的软分解。基于此初始化，我们引入几何和运动约束细化，通过可学习的运动学基元（由关节轴、枢轴点和每帧运动标量参数化）强制执行物理上合理的关节运动，从而产生几何准确且时间一致的 reconstruction。大量实验表明，Articulat3D在合成基准和真实世界随意捕获的单目视频上实现了最先进的性能，显著推进了在不受控现实条件下创建数字孪生的可行性。我们的项目页面位于此 https URL。
英文摘要：
Building high-fidelity digital twins of articulated objects from visual data remains a central challenge. Existing approaches depend on multi-view captures of the object in discrete, static states, which severely constrains their real-world scalability. In this paper, we introduce Articulat3D, a novel framework that constructs such digital twins from casually captured monocular videos by jointly enforcing explicit 3D geometric and motion constraints. We first propose Motion Prior-Driven Initialization, which leverages 3D point tracks to exploit the low-dimensional structure of articulated motion. By modeling scene dynamics with a compact set of motion bases, we facilitate soft decomposition of the scene into multiple rigidly moving groups. Building on this initialization, we introduce Geometric and Motion Constraints Refinement, which enforces physically plausible articulation through learnable kinematic primitives parameterized by a joint axis, a pivot point, and per-frame motion scalars, yielding reconstructions that are both geometrically accurate and temporally coherent. Extensive experiments demonstrate that Articulat3D achieves state-of-the-art performance on synthetic benchmarks and real-world casually captured monocular videos, significantly advancing the feasibility of digital twin creation under uncontrolled real-world conditions. Our project page is available at https://maxwell-zhao.github.io/Articulat3D/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
