Liquid Neural Networks as a Drop-in Continuous-Time Deformation Field for Dynamic 3D Gaussian Splatting
arXiv:2606.07670v1 Announce Type: cross Abstract: Deformable 3D Gaussian Splatting (D-3DGS) re-constructs dynamic scenes from monocular video by deforming a canonical set of 3D Gaussians through a positional-encoded MLP of frame time t. Although fitted to a continuous variable, the MLP couples no two values of t in its architecture and effectively predicts discrete per-frame offsets, leaving temporal smoothness to emerge only as a byproduct of optimisation. We redesign the deformation field as a stack of Closed-form Continuous-time (CfC) cells, a Liquid Neural Network (LNN), that is the closed
The continuous evolution of computational efficiency and architectural innovation in neural networks allows for more sophisticated techniques to enhance real-time 3D scene reconstruction and dynamic representation.
This development allows for more accurate and computationally efficient real-time reconstruction of dynamic 3D scenes, crucial for advancements in robotics, augmented reality, and simulations.
The use of Liquid Neural Networks (LNNs) provides a more inherently continuous and temporally smooth deformation field for dynamic 3D Gaussian Splatting, moving beyond discrete per-frame offsets.
- · AI researchers
- · Robotics companies
- · AR/VR developers
- · Simulation and gaming industry
- · Developers reliant on less temporally smooth 3D reconstruction methods
- · Hardware limited by computational demands of current 3DGS approaches
Improved fidelity and real-time performance in dynamic 3D scene capture and rendering.
Accelerated development of more adaptive and context-aware AI systems, particularly in embodied AI and autonomous agents.
Enhanced human-computer interaction through seamless integration of digital content into physical environments, leading to new forms of entertainment and productivity.
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Read at arXiv cs.AI