Reconstructing dynamic 3D scenes from monocular videos remains a fundamental challenge in 3D vision. While 3D Gaussian Splatting (3DGS) achieves real-time rendering in static settings, extending it to dynamic scenes is challenging due to the difficulty of learning structured and temporally consistent motion representations. This challenge often manifests as three limitations in existing methods: redundant Gaussian updates, insufficient motion supervision, and weak modeling of complex non-rigid deformations. These issues collectively hinder coherent and efficient dynamic reconstruction. To address these limitations, we propose HAIF-GS, a unified framework that enables structured and consistent dynamic modeling through sparse anchor-driven deformation. It first identifies motion-relevant regions via an Anchor Filter to suppress redundant updates in static areas. A self-supervised Induced Flow-Guided Deformation module induces anchor motion using multi-frame feature aggregation, eliminating the need for explicit flow labels. To further handle fine-grained deformations, a Hierarchical Anchor Propagation mechanism increases anchor resolution based on motion complexity and propagates multi-level transformations. Extensive experiments on synthetic and real-world benchmarks validate that HAIF-GS significantly outperforms prior dynamic 3DGS methods in rendering quality, temporal coherence, and reconstruction efficiency.

翻译：从单目视频重建动态三维场景仍是三维视觉领域的核心挑战。尽管三维高斯溅射（3DGS）在静态场景中实现了实时渲染，但将其扩展至动态场景仍面临困难，主要源于学习结构化且时序一致的运动表征的复杂性。这一挑战通常表现为现有方法的三个局限：高斯更新的冗余性、运动监督的不足性以及对复杂非刚性形变建模的薄弱性。这些问题共同阻碍了连贯且高效的动态重建。为应对这些局限，我们提出HAIF-GS——一个通过稀疏锚点驱动形变实现结构化一致性动态建模的统一框架。该框架首先通过锚点过滤器识别运动相关区域，以抑制静态区域的冗余更新；其次，自监督的诱导流引导形变模块利用多帧特征聚合来推导锚点运动，无需显式的光流标签；为进一步处理细粒度形变，分层锚点传播机制基于运动复杂度提升锚点分辨率，并传播多层级变换。在合成与真实场景基准上的大量实验验证表明，HAIF-GS在渲染质量、时序一致性与重建效率方面显著优于现有动态3DGS方法。