SkeletonGaussian: 通过高斯骨架化实现可编辑的4D生成
SkeletonGaussian: Editable 4D Generation through Gaussian Skeletonization
AI总结：
SkeletonGaussian通过高斯骨架化实现可编辑的4D生成，利用分层可动表示和六平面细化提升生成质量与编辑能力。
摘要：
4D生成在从输入文本、图像或视频合成动态3D对象方面取得了显著进展。然而，现有方法往往将运动表示为隐式变形场，这限制了直接控制和可编辑性。为了解决这个问题，我们提出了SkeletonGaussian，一种新的框架，用于从单目视频输入生成可编辑的动态3D高斯。我们的方法引入了一种分层的可动表示，将运动分解为由骨架显式驱动的稀疏刚体运动和细粒度非刚体运动。具体来说，我们提取了一个稳健的骨架并通过线性混合皮肤来驱动刚体运动，随后通过六平面基于的细化来处理非刚体变形，增强了可解释性和可编辑性。实验结果表明，SkeletonGaussian在生成质量上超过了现有方法，同时实现了直观的运动编辑，为可编辑的4D生成建立了一个新的范式。项目页面：https://wusar.github.io/projects/skeletongaussian/
英文摘要：
4D generation has made remarkable progress in synthesizing dynamic 3D objects from input text, images, or videos. However, existing methods often represent motion as an implicit deformation field, which limits direct control and editability. To address this issue, we propose SkeletonGaussian, a novel framework for generating editable dynamic 3D Gaussians from monocular video input. Our approach introduces a hierarchical articulated representation that decomposes motion into sparse rigid motion explicitly driven by a skeleton and fine-grained non-rigid motion. Concretely, we extract a robust skeleton and drive rigid motion via linear blend skinning, followed by a hexplane-based refinement for non-rigid deformations, enhancing interpretability and editability. Experimental results demonstrate that SkeletonGaussian surpasses existing methods in generation quality while enabling intuitive motion editing, establishing a new paradigm for editable 4D generation. Project page: https://wusar.github.io/projects/skeletongaussian/
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。
