StyleMe3D: Stylization with Disentangled Priors by Multiple Encoders on
  3D Gaussians
Authors: name: Cailin Zhuang, name: Yaoqi Hu, name: Xuanyang Zhang, name: Wei Cheng, name: Jiacheng Bao, name: Shengqi Liu, name: Yiying Yang, name: Xianfang Zeng, name: Gang Yu, name: Ming Li

3D Gaussian Splatting (3DGS) excels in photorealistic scene reconstruction
but struggles with stylized scenarios (e.g., cartoons, games) due to fragmented
textures, semantic misalignment, and limited adaptability to abstract
aesthetics. We propose StyleMe3D, a holistic framework for 3D GS style transfer
that integrates multi-modal style conditioning, multi-level semantic alignment,
and perceptual quality enhancement. Our key insights include: (1) optimizing
only RGB attributes preserves geometric integrity during stylization; (2)
disentangling low-, medium-, and high-level semantics is critical for coherent
style transfer; (3) scalability across isolated objects and complex scenes is
essential for practical deployment. StyleMe3D introduces four novel components:
Dynamic Style Score Distillation (DSSD), leveraging Stable Diffusion's latent
space for semantic alignment; Contrastive Style Descriptor (CSD) for localized,
content-aware texture transfer; Simultaneously Optimized Scale (SOS) to
decouple style details and structural coherence; and 3D Gaussian Quality
Assessment (3DG-QA), a differentiable aesthetic prior trained on human-rated
data to suppress artifacts and enhance visual harmony. Evaluated on NeRF
synthetic dataset (objects) and tandt db (scenes) datasets, StyleMe3D
outperforms state-of-the-art methods in preserving geometric details (e.g.,
carvings on sculptures) and ensuring stylistic consistency across scenes (e.g.,
coherent lighting in landscapes), while maintaining real-time rendering. This
work bridges photorealistic 3D GS and artistic stylization, unlocking
applications in gaming, virtual worlds, and digital art.