骨架高斯：基于高斯骨架化的可编辑四维生成

摘要

四维生成技术在从输入文本、图像或视频合成动态三维物体方面取得了显著进展。然而，现有方法通常将运动表示为隐式变形场，这限制了直接控制与编辑能力。为解决这一问题，我们提出SkeletonGaussian——一种从单目视频输入生成可编辑动态三维高斯点云的新框架。该方法引入分层铰接式表征，将运动显式分解为由骨骼驱动的稀疏刚性运动与细粒度非刚性运动。具体而言，我们通过提取鲁棒骨骼架构并利用线性混合蒙皮驱动刚性运动，再结合基于六平面结构的非刚性形变优化，显著提升了系统的可解释性与可编辑性。实验结果表明，SkeletonGaussian在生成质量上超越现有方法，同时支持直观的运动编辑，为可编辑四维生成建立了新范式。项目页面：https://wusar.github.io/projects/skeletongaussian/

English

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/

骨架高斯：基于高斯骨架化的可编辑四维生成

SkeletonGaussian: Editable 4D Generation through Gaussian Skeletonization

摘要

Support