arXiv:2605.07385cs.GRcs.CV2026-05被引 1

无需训练和掩码,通过修改采样器内部速度场实现3D资产精准局部编辑。

Velocity-Space 3D Asset Editing

论文配图:Velocity-Space 3D Asset Editing
图 1 · 摘自论文原文
  • 在采样器内部直接干预速度场,解决局部编辑的根源问题。
  • 三模块协同:去泄漏、放大编辑信号、动态保留关键特征。
  • 适合需要高精度3D内容修改的设计师与开发者使用。

对3D资产进行局部编辑,即修改特定区域同时保持其余部分不变,是原生3D编辑的基本需求。现有方法依赖生成器外部机制(如手动3D掩码、后处理体素合并或2D多视角提升),未能触及问题本质——位于常微分方程(ODE)采样器内部的扰动源。为使校正流生成器实现忠实的局部编辑,其速度场应在目标编辑区域强烈作用,而在保留区域趋于零。但单一速度场难以同时满足两者,导致三大问题:(i) 身份泄露,编辑信号在保留区域仍非零;(ii) 缺乏专用编辑增强通道,强化编辑必然扰动身份;(iii) 在几何与材质阶段存在身份拖拽,全局条件将每个令牌拉向目标。本文提出VS3D(Velocity-Space 3D Asset Editing),一种无反演、免训练、无掩码的框架,通过在采样器内针对性干预,分别解决上述问题。VS3D集成三个互补模块:重建锚定源注入(RASI)通过源重建校准每步资产特异性锚点,消除身份泄露;局部均值引导(PMG)通过对比高质量与低质量子样本的速度差估计,仅在存在一致编辑时激活以放大编辑信号;双一致性残差注入(TAR)让采样器逐标记决定在几何与材质阶段应保留什么。该方法实现高效、精准、无需额外输入的3D资产编辑。

原文摘要 · Abstract (English)

Editing a 3D asset locally, modifying a target region while preserving the rest, is a fundamental requirement of native 3D editing. Existing methods enforce locality through mechanisms external to the generator, such as manual 3D masks, post-hoc voxel merging, or 2D multi-view lifting. None of them intervene where the corruption actually originates: inside the ODE sampler. For a rectified-flow generator to achieve faithful local editing, its velocity field should be strong over the target editing region while vanishing on preserved content. Yet a single velocity field can hardly satisfy both requirements simultaneously, leading to three problems: (i) identity leakage that keeps the edit signal non-zero on preserved regions; (ii) no dedicated edit-amplification channel, so strengthening the edit inevitably perturbs identity; and (iii) an identity drag at the geometry and material stages, where a global condition pulls every token toward the target. We propose VS3D (Velocity-Space 3D Asset editing}), an inversion-free, training-free, and mask-free framework that addresses each problem with a targeted intervention inside the sampler. VS3D integrates three complementary modules, each corresponding to a specific stage of the editing pipeline. Reconstruction-Anchored Source Injection (RASI) absorbs identity leakage by turning the unconditional embedding into a per-step, asset-specific anchor calibrated through source reconstruction. Partial-Mean Guidance (PMG) amplifies the edit signal by contrasting high- and low-quality subsample estimates of the velocity difference, active only where a consistent edit exists. Twin-Agreement Residual injection (TAR) lets the sampler decide token by token what to preserve at the geometry and material stages.

3D编辑扩散模型速度场无训练

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