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arXiv 2606.07670cs.CVcs.AI

液态神经网络作为动态3D高斯泼溅的即插即用连续时间变形场

Liquid Neural Networks as a Drop-in Continuous-Time Deformation Field for Dynamic 3D Gaussian Splatting

Monash University(莫纳什大学)

Mingzhao Li, Arghya Pal, Guan Yuan Tan

AI总结:

提出用液态神经网络(LNN)的闭式连续时间(CfC)单元替代MLP,构建显式连续时间变形场,在动态场景重建中匹配或超越MLP基线,尤其擅长高频关节运动。

摘要:

可变形3D高斯泼溅(D-3DGS)通过一个位置编码的MLP(以帧时间t为输入)变形一组规范3D高斯,从单目视频重建动态场景。尽管拟合连续变量,但MLP在架构中不耦合任意两个t值,实际上预测离散的逐帧偏移,使得时间平滑性仅作为优化的副产品出现。我们将变形场重新设计为一组闭式连续时间(CfC)单元,即液态神经网络(LNN),它是液态时间常数ODE的闭式解,同时保留D-3DGS管道的其他部分。每个单元暴露一个sigmoid时间门,在两个候选隐藏状态之间插值,将学习到的对t的平滑响应嵌入损失景观,无需调用任何数值求解器。在八个D-NeRF和七个NeRF-DS场景上,液态场在总体上匹配或超过MLP基线,其最大增益集中在具有最高频关节运动的场景上。结果是一种近乎零摩擦的架构设计,将离散的MLP变形场转变为t的显式连续时间函数。

英文摘要:

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-form solution of the Liquid Time-constant ODE while preserving every other part of the D-3DGS pipeline. Each cell exposes a sigmoidal time gate that interpolates between two candidate hidden states, baking a learned smooth response to t into the loss landscape without invoking any numerical solver. On the eight D-NeRF and seven NeRF-DS scenes the liquid field matches or exceeds the MLP baseline in aggregate, with its largest gains concentrated on the scenes with the most high-frequency articulated motion. The result is a near-zero-friction architectural design that turns the discrete MLP deformation field into an explicit continuous-time function of t.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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