用稀疏体素谱实现真实树木的实时动态交互动画
DynamicTree: Interactive Real Tree Animation via Sparse Voxel Spectrum
- 用稀疏体素谱表示树体运动，实现快速前向生成
- 生成8786个带100帧动画的合成树数据集，支持长时序模拟
- 支持外力下的实时模态分析，适合虚拟现实与游戏应用
生成动态且可交互的3D树木在虚拟现实、游戏和世界模拟中有广泛应用。然而，现有方法在生成复杂真实树木的结构一致且逼真的4D运动方面仍面临挑战。本文提出DynamicTree，首个能为真实树木的3DGS重建生成长期、可交互3D运动的框架。不同于以往基于优化的方法，本方法采用快速前馈方式生成动态。其关键在于使用紧凑的稀疏体素谱表示树体运动。给定来自Gaussian Splatting重建的3D树，管道首先利用稀疏体素谱生成网格运动，再将高斯点绑定以形变网格。此外，该稀疏体素谱还可作为外部力作用下快速模态分析的基础，实现实时交互响应。为训练模型，我们还构建了4DTree，首个大规模合成4D树数据集，包含8,786个带100帧运动序列的动画树网格。大量实验表明，该方法在视觉质量和计算效率上显著优于现有方法。
原文摘要 · Abstract (English)
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.
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