VideoArtGS：从单目视频构建关节物体的数字孪生
VideoArtGS: Building Digital Twins of Articulated Objects from Monocular Video
AI总结：
VideoArtGS 提出利用运动先验引导流程和混合中心-网格部件分配模块，从单目视频高保真重建关节物体数字孪生，将重建误差降低约两个数量级。
摘要：
从单目视频构建关节物体的数字孪生是计算机视觉中的一个重要挑战，它要求在有限视角输入下同时重建物体几何、部件分割和关节参数。单目视频因其简单性和可扩展性而成为一种有吸引力的输入形式；然而，仅依靠视觉监督来解耦物体几何与部件动态十分困难，因为相机与部件的联合运动会导致不适定估计。虽然来自预训练跟踪模型的运动先验可以缓解这一问题，但如何有效整合这些先验以进行关节学习在很大程度上仍未得到探索。为了解决这一问题，我们提出了 VideoArtGS，这是一种从单目视频重建高保真关节物体数字孪生的新方法。我们提出了一条运动先验引导流程，该流程分析 3D 轨迹、过滤噪声，并提供可靠的关节参数初始化。我们还设计了一个用于基于关节的变形场的混合中心-网格部件分配模块，以捕获准确的部件运动。VideoArtGS 在关节和网格重建方面展示了最先进的性能，与现有方法相比，重建误差降低了约两个数量级。VideoArtGS 实现了从单目视频进行实用的数字孪生创建，为基于视频的关节物体重建建立了新的基准。我们的工作已公开发布于：https://videoartgs.github.io。
英文摘要：
Building digital twins of articulated objects from monocular video presents an essential challenge in computer vision, which requires simultaneous reconstruction of object geometry, part segmentation, and articulation parameters from limited viewpoint inputs. Monocular video offers an attractive input format due to its simplicity and scalability; however, it's challenging to disentangle the object geometry and part dynamics with visual supervision alone, as the joint movement of the camera and parts leads to ill-posed estimation. While motion priors from pre-trained tracking models can alleviate the issue, how to effectively integrate them for articulation learning remains largely unexplored. To address this problem, we introduce VideoArtGS, a novel approach that reconstructs high-fidelity digital twins of articulated objects from monocular video. We propose a motion prior guidance pipeline that analyzes 3D tracks, filters noise, and provides reliable initialization of articulation parameters. We also design a hybrid center-grid part assignment module for articulation-based deformation fields that captures accurate part motion. VideoArtGS demonstrates state-of-the-art performance in articulation and mesh reconstruction, reducing the reconstruction error by about two orders of magnitude compared to existing methods. VideoArtGS enables practical digital twin creation from monocular video, establishing a new benchmark for video-based articulated object reconstruction. Our work is made publicly available at: https://videoartgs.github.io.
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。
