StyleMe3D: Stylization with Disentangled Priors by Multiple Encoders on 3D Gaussians.
Published in arXiv, 2025
Current 3D Gaussian Splatting (3DGS) stylization approaches are limited in their ability to represent diverse artistic styles, frequently defaulting to low-level texture replacement or yielding semantically inconsistent outputs.
In this paper, we introduce StyleMe3D, a novel hierarchical framework that achieves comprehensive, high-fidelity stylization by disentangling multi-level style representations while preserving geometric fidelity. The cornerstone of StyleMe3D is Dynamic Style Score Distillation (DSSD), which harnesses latent priors from a style-aware diffusion model to provide high-level semantic guidance. We further propose a Contrastive Style Descriptor (CSD) for middle-level stylistic similarity and a 3D Gaussian Quality Assessment (3DG-QA) to enhance perceptual quality. Finally, a Simultaneously Optimized Scale (SOS) module is integrated to refine fine-grained texture details at the low-level.