DiDE:Direct Injection with Color-Texture DEcoupling for 3D Stylization
Recent advances in rectified flow-based image-to-3D generative models have enabled high-fidelity 3D asset generation. Building on this, a growing line of work has exploited these strong 3D priors for training-free stylization, transferring visual attributes from a reference image onto a generated 3D asset. However, existing methods enforce an all-or-nothing paradigm: color and texture are transferred jointly, with no mechanism to control them independently -- a limitation we formalize as Disentangled 3D Stylization(Disen3D). To address this, we propose DiDE, the first training-free framework for Disen3D. Key to our approach is the observation that the structured latent space of image-to-3...
| Authors | Tao Wu, Alexandra Gomez-Villa, Senmao Li et al. |
|---|---|
| Primary category | cs.CV |
| Published | 1 October 2026 |
| Last updated at source | 6 October 2026 |
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