TanGO: Training-Free 3D Editing via Tangent-Space Guidance and Optimization

Abstract

While recent flow-matching 3D generative models (e.g., Vec-Set) adopt structured representations, their tokens share global context,causing conventional training-free editing to suffer from semantic arti-facts such as collapsed preserved regions or incomplete transformations.To address this, we propose TanGO, a training-free framework thatenables adaptive per-token steering in the tangent space of generativedynamics. To realize this selective control, we formulate a one-step opti-mal control rule and determine the strength of each token’s control signalusing a von Mises-Fisher inspired directional discrepancy derived fromthe source and target velocity fields. Experiments show that TanGOsubstantially reduces structural artifacts and achieves state-of-the-artperformance, outperforming existing 3D editing baselines. The code ispublicly available at https://github.com/siw00-lim/TanGO.