DynamicTree: Interactive Real Tree Animation via Sparse Voxel Spectrum

2025-10-25

AI Summary

This work addresses the challenges of generating photorealistic 4D (3D + time) tree animations and achieving low-latency interactivity in virtual reality and world simulation. We propose the first sparse voxel spectral representation for tree motion, enabling feedforward animation synthesis and modal analysis. Coupled with a 3D Gaussian splatting-based skeletal deformation technique, our method achieves efficient geometric reconstruction. We train an end-to-end neural network on a large-scale synthetic 4DTree dataset curated for this purpose. Compared to conventional optimization-based approaches, our framework significantly improves computational efficiency and visual fidelity: it supports real-time interaction, long-term deformation coherence, and outperforms prior methods across key metrics—including dynamic realism, frame rate, and memory footprint. The core innovations are (1) sparse voxel spectral modeling of tree motion dynamics and (2) a lightweight, Gaussian-splatting-driven 4D tree animation generation framework.

Technology Category

• Computer Vision: Motion & Tracking
• Search and Optimization: Sampling/Simulation-based Search
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Web Mining and Content Analysis: Web data generation and simulation

Abstract

Generating dynamic and interactive 3D objects, such as trees, has wide applications in virtual reality, games, and world simulation. Nevertheless, existing methods still face various challenges in generating realistic 4D motion for complex real trees. In this paper, we propose DynamicTree, the first framework that can generate long-term, interactive animation of 3D Gaussian Splatting 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 semantic labels and 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.

Problem

• Generating realistic 4D motion for complex real trees
• Creating interactive tree animations in real-time
• Representing tree movement using sparse voxel spectrum

Innovation

• Uses sparse voxel spectrum for tree movement representation
• Generates dynamics in fast feed-forward manner
• Enables real-time interactive responses via modal analysis