将高质量3D编辑迁移到动态4D场景，保持时空一致
Catalyst4D: High-Fidelity 3D-to-4D Scene Editing via Dynamic Propagation
- 用结构稳定锚点构建区域级参考，通过最优传输实现无干扰变形传播
- 基于高斯颜色不确定性选择性修复遮挡伪影，提升时序外观一致性
- 适合需要真实动态场景编辑的视觉生成与数字孪生研究者
基于NeRF和3DGS的3D场景编辑技术已实现高质量静态场景编辑。然而，动态场景编辑仍具挑战性，直接扩展2D扩散模型至4D常导致运动伪影、时间闪烁和风格传播不一致。本文提出Catalyst4D框架，可将高质量3D编辑迁移至动态4D高斯场景，同时保持空间与时间的一致性。核心方法包括：基于锚点的运动引导（AMG），从原始与编辑后的高斯分布中提取结构稳定且空间具有代表性的锚点，通过最优传输建立对应关系，实现无跨区域干扰与运动漂移的一致性形变传播；以及颜色不确定性引导的外观精炼（CUAR），通过估计每个高斯的颜色不确定性，选择性地精炼易受遮挡影响的区域，从而维持时序外观一致性。大量实验表明，Catalyst4D在视觉质量与运动连贯性上均优于现有方法，实现了时序稳定的高保真动态场景编辑。
原文摘要 · Abstract (English)
Recent advances in 3D scene editing using NeRF and 3DGS enable high-quality static scene editing. In contrast, dynamic scene editing remains challenging, as methods that directly extend 2D diffusion models to 4D often produce motion artifacts, temporal flickering, and inconsistent style propagation. We introduce Catalyst4D, a framework that transfers high-quality 3D edits to dynamic 4D Gaussian scenes while maintaining spatial and temporal coherence. At its core, Anchor-based Motion Guidance (AMG) builds a set of structurally stable and spatially representative anchors from both original and edited Gaussians. These anchors serve as robust region-level references, and their correspondences are established via optimal transport to enable consistent deformation propagation without cross-region interference or motion drift. Complementarily, Color Uncertainty-guided Appearance Refinement (CUAR) preserves temporal appearance consistency by estimating per-Gaussian color uncertainty and selectively refining regions prone to occlusion-induced artifacts. Extensive experiments demonstrate that Catalyst4D achieves temporally stable, high-fidelity dynamic scene editing and outperforms existing methods in both visual quality and motion coherence.
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