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arXiv · 2608.03179

EditFlow3D: Automated Local Editing of 3D Assets with Trajectory Preservation

Rui Nie·Chuang Wang·Haitao Zhou·Jiahe Song·Buyu Li·Sheng Wang·Qian Yu
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Abstract

Controllable local editing of 3D assets requires precise target localization and appropriate visual guidance. However, existing methods lack a simple yet accurate way to obtain 3D masks and struggle to achieve the desired edit while faithfully preserving the structure and appearance of non-target regions. To address these challenges, we present EditFlow3D, a training-free framework for local 3D editing. Given a source asset and an edit instruction, a VLM-driven workflow interprets the editing intent and automatically constructs a visual guidance image and a refined 3D editing mask, enabling localized editing in the native representation space of a pretrained 3D generative model. Specifically, mask-guided differential flow focuses the edit on the target region, while step-wise trajectory preservation maintains consistency between non-target regions and the source asset without directly replacing intermediate features. Since the existing Edit3D-Bench covers only a limited range of local editing categories, we further introduce EditFlow-Bench as a complementary benchmark encompassing a broader variety of structural and appearance edits, and evaluate EditFlow3D on both benchmarks. Quantitative results, qualitative comparisons, and a user study demonstrate that EditFlow3D achieves more accurate target-region editing and better preserves non-target regions than existing 3D editing methods.

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Rui Nie, Chuang Wang, Haitao Zhou, Jiahe Song, Buyu Li, Sheng Wang, Qian Yu. 2026-08-04. EditFlow3D: Automated Local Editing of 3D Assets with Trajectory Preservation. https://arxiv.org/abs/2608.03179

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