用运动轨迹重参数化高斯形状，解决模糊单目动态场景重建难题
Kinematics-Driven Gaussian Shape Deformation for Blurry Monocular Dynamic Scenes
- 将运动模糊建模为沿运动轨迹的形变，引入运动先验优化高斯形状
- 在真实模糊视频上实现优于现有方法的重建精度，尤其在非刚性运动中表现突出
- 适合研究动态3D重建、视觉几何与运动估计的学者使用
从模糊单目视频中重建动态三维场景极具挑战，因运动模糊会混淆物体运动与几何信息，破坏几何一致性。本文提出Kinematics-GS，一种基于运动感知的框架，将模糊建模为与运动对齐的形变，并引入运动先验来重参数化高斯形状，从而在无需额外运动监督的情况下缓解形状坍缩问题。为稳定优化，利用时间形变方差将场景分解为动态与静态成分，并采用粗到细的形变策略以捕捉全局运动与细微结构。此外，我们构建了一个包含可变形弹性物体的真实世界数据集，其具有空间非均匀运动模糊，遮蔽几何线索。在多个含真实运动模糊的真实世界基准上的大量实验表明，Kinematics-GS显著优于先前方法，在复杂非刚性运动场景中展现出更强鲁棒性。
原文摘要 · Abstract (English)
Reconstructing dynamic 3D scenes from blurry monocular videos is challenging as motion-induced blur entangles object motion and geometry, hindering geometric consistency. We present Kinematics-GS, a kinematics-aware framework that models blur as motion-aligned deformation and introduces a kinematic prior to reparameterize Gaussian shapes along motion trajectories, thereby mitigating degenerate shape collapse without auxiliary motion supervision. To stabilize optimization, we decompose scenes into dynamic and static components using temporal deformation variance and employ a coarse-to-fine deformation strategy to capture both global motion and fine-grained details. We also introduce a challenging real-world dataset of deformable and elastic objects exhibiting non-rigid motion with spatially non-uniform motion blur that obscures geometric cues. Extensive experiments on real-world benchmarks with realistic motion blur demonstrate that Kinematics-GS outperforms prior methods by a clear margin in monocular dynamic scene reconstruction, highlighting its effectiveness in handling complex and non-rigid motion scenarios.
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