FlowMorpher: Inversion-Free Textured 3D Morphing via Velocity Field Interpolation

Textured 3D morphing aims to achieve smooth changes in geometry and appearance while recovering both endpoints. However, existing training-free approaches require access to model internals for feature matching and interpolation, and often exhibit abrupt transitions. Other methods require costly caching of intermediate latent states and attention features obtained through 3D inversion. To address these gaps, we introduce FlowMorpher, a training-free and inversion-free method that constructs sampling dynamics by interpolating conditional velocity fields of pretrained image-to-3D flow models without modifying their internals. FlowMorpher constructs sampling trajectories directly from shared Gaussian noise, eliminating the need for inversion. In addition, we define the sampling update rule by interpolating the model's conditional velocity predictions, without modifying internal attention operations. Under the stated regularity assumptions, our formulation guarantees Lipschitz continuity of the terminal latent with respect to the morphing weight within a fixed latent space. This analysis provides theoretical support for smooth transitions by establishing latent continuity under the stated assumptions. To evaluate FlowMorpher, we assemble MorphEdit3D from existing datasets and benchmarks, covering color, shape, and semantic changes. Our quantitative results improve over the strongest baseline on every reported metric, reducing KID by 11.2% and PDV by 21.4% while raising target-endpoint DINO-I by 0.039. Qualitative comparisons show intermediate assets with smooth transitions in the generated 3D sequences. These findings show that textured 3D morphing can be controlled through sampling dynamics defined by pretrained velocity predictions.