While novel view synthesis for dynamic scenes has made significant progress, capturing skeleton models of objects and re-posing them remains a challenging task. To tackle this problem, in this paper, we propose a novel approach to automatically discover the associated skeleton model for dynamic objects from videos without the need for object-specific templates. Our approach utilizes 3D Gaussian Splatting and superpoints to reconstruct dynamic objects. Treating superpoints as rigid parts, we can discover the underlying skeleton model through intuitive cues and optimize it using the kinematic model. Besides, an adaptive control strategy is applied to avoid the emergence of redundant superpoints. Extensive experiments demonstrate the effectiveness and efficiency of our method in obtaining re-posable 3D objects. Not only can our approach achieve excellent visual fidelity, but it also allows for the real-time rendering of high-resolution images.
尽管动态场景的新视角合成取得了显著进展，但捕获对象的骨架模型并对其重新定位仍然是一项具有挑战性的任务。为解决这一问题，本文提出了一种新颖的方法，能够无需对象特定模板，自动从视频中发现动态对象的相关骨架模型。 我们的方法利用3D高斯点云（3D Gaussian Splatting）和超点（superpoints）重建动态对象。将超点视为刚性部件，我们通过直观的线索发现底层骨架模型，并通过运动学模型进行优化。此外，我们应用了一种自适应控制策略，以避免冗余超点的出现。 大量实验表明，我们的方法在获取可重新定位的3D对象方面表现出色，不仅能够实现优异的视觉保真度，还支持高分辨率图像的实时渲染。这表明该方法在动态对象的骨架建模和重定位任务中具有高效性和有效性。