Generating dynamic and interactive 3D trees has wide applications in virtual reality, games, and world simulation. However, existing methods still face various challenges in generating structurally consistent and realistic 4D motion for complex real trees. In this paper, we propose DynamicTree, the first framework that can generate long-term, interactive 3D motion for 3DGS reconstructions of real trees. Unlike prior optimization-based methods, our approach generates dynamics in a fast feed-forward manner. The key success of our approach is the use of a compact sparse voxel spectrum to represent the tree movement. Given a 3D tree from Gaussian Splatting reconstruction, our pipeline first generates mesh motion using the sparse voxel spectrum and then binds Gaussians to deform the mesh. Additionally, the proposed sparse voxel spectrum can also serve as a basis for fast modal analysis under external forces, allowing real-time interactive responses. To train our model, we also introduce 4DTree, the first large-scale synthetic 4D tree dataset containing 8,786 animated tree meshes with 100-frame motion sequences. Extensive experiments demonstrate that our method achieves realistic and responsive tree animations, significantly outperforming existing approaches in both visual quality and computational efficiency.

翻译：生成动态且交互式的三维树木在虚拟现实、游戏和世界仿真中具有广泛的应用。然而，现有方法在生成结构一致且逼真的复杂真实树木四维运动方面仍面临诸多挑战。本文提出了DynamicTree，这是首个能够为真实树木的3DGS重建生成长期、交互式三维运动的框架。与先前基于优化的方法不同，我们的方法以前馈方式快速生成动态效果。该方法成功的关键在于使用紧凑的稀疏体素谱来表示树木运动。给定来自高斯泼溅重建的三维树木，我们的流程首先利用稀疏体素谱生成网格运动，随后将高斯元素绑定至网格以驱动其形变。此外，所提出的稀疏体素谱还可作为外力作用下快速模态分析的基础，从而实现实时交互响应。为训练模型，我们还引入了4DTree——首个大规模合成四维树木数据集，包含8,786个具有100帧运动序列的动画树木网格。大量实验表明，我们的方法能够实现逼真且响应灵敏的树木动画，在视觉质量和计算效率方面均显著优于现有方法。