MaterialFlow: Attribute-Disentangled Material Transfer via Trajectory-Aware Velocity Modulation

Material transfer seeks to re-render an object’s surface usinga reference exemplar while preserving its original geometry and identity.Current training-free methods often rely on attention-based injection,which entangles attributes like color, texture, and structural patternsinto a single representation. This entanglement leads to structural in-stability and imprecise results. Additionally, inversion-based editing inflow models is prone to reconstruction-induced trajectory drift, whereaccumulated errors degrade object details. We propose MaterialFlow, atraining-free framework for precise material transfer using pre-trainedflow models. Our approach introduces a trajectory-aware velocity modu-lation mechanism that rebalances the generative flow in an inversion-free manner. This ensures stable and semantically consistent editingdynamics without the need for explicit latent reconstruction. We fur-ther introduce an attribute-aware disentanglement paradigm that sepa-rates reference materials into color, texture, and meso-scale patterns forfine-grained control. Evaluations on the Material Transfer Benchmark(MTB) show that MaterialFlow surpasses state-of-the-art methods intransfer quality, inference efficiency, and controllability. Project page:https://github.com/Sung-Lin/MaterialFlow.