StyleMe3D: Stylization with Disentangled Priors by Multiple Encoders on
  3D Gaussians
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.
68072d05362af0cf18fb4b0c
Cailin Zhuang
68072d05362af0cf18fb4b0d
Yaoqi Hu
68072d05362af0cf18fb4b0e
Xuanyang Zhang
admin_assigned
2025-04-22T10:53:34.744Z
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xuanyang zhang
user
xuanyangz
68072d05362af0cf18fb4b0f
Wei Cheng
claimed_verified
2025-04-22T09:50:44.772Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/64b914c8ace99c0723ad83a9/B4gxNByeVY_xaOcjwiN1j.jpeg
Wei Cheng
user
wchengad
68072d05362af0cf18fb4b10
Jiacheng Bao
admin_assigned
2025-04-22T10:53:41.233Z
6592960e0f4519bfc2dd4968
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/-9JT886u9hepkN3IX-875.png
Jia Cheng Bao
user
unpackableorange
68072d05362af0cf18fb4b11
Shengqi Liu
68072d05362af0cf18fb4b12
Yiying Yang
claimed_verified
2025-04-22T09:50:42.183Z
67da6acc05101e8e1d2c20a2
/avatars/1cfa3a1f59687db58af4e1b4a8767bfd.svg
Yang
user
Yiying12
68072d05362af0cf18fb4b13
Xianfang Zeng
68072d05362af0cf18fb4b14
Gang Yu
68072d05362af0cf18fb4b15
Ming Li
StyleMe3D: Stylization with Disentangled Priors by Multiple Encoders on
  3D Gaussians
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.
StyleMe3D enhances 3D Gaussian Splatting for stylized scene reconstruction by integrating multi-modal style conditioning, semantic alignment, and perceptual quality enhancement.
StyleMe3D 3D Gaussian Splatting multi-modal style conditioning multi-level semantic alignment perceptual quality enhancement Dynamic Style Score Distillation Stable Diffusion Contrastive Style Descriptor Simultaneously Optimized Scale 3D Gaussian Quality Assessment NeRF tandt db real-time rendering