title: StyleMe3D: Stylization with Disentangled Priors by Multiple Encoders on
    3D Gaussians
summary: 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.
paper_authors._id: 68072d05362af0cf18fb4b0c
paper_authors.name: Cailin Zhuang
paper_authors._id: 68072d05362af0cf18fb4b0d
paper_authors.name: Yaoqi Hu
paper_authors._id: 68072d05362af0cf18fb4b0e
paper_authors.name: Xuanyang Zhang
paper_authors.status: admin_assigned
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paper_authors.fullname: xuanyang zhang
paper_authors.type: user
paper_authors.user: xuanyangz
paper_authors._id: 68072d05362af0cf18fb4b0f
paper_authors.name: Wei Cheng
paper_authors.status: claimed_verified
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paper_authors.fullname: Wei Cheng
paper_authors.type: user
paper_authors.user: wchengad
paper_authors._id: 68072d05362af0cf18fb4b10
paper_authors.name: Jiacheng Bao
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-22T10:53:41.233Z
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paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/-9JT886u9hepkN3IX-875.png
paper_authors.fullname: Jia Cheng Bao
paper_authors.type: user
paper_authors.user: unpackableorange
paper_authors._id: 68072d05362af0cf18fb4b11
paper_authors.name: Shengqi Liu
paper_authors._id: 68072d05362af0cf18fb4b12
paper_authors.name: Yiying Yang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-22T09:50:42.183Z
paper_authors._id: 67da6acc05101e8e1d2c20a2
paper_authors.avatarUrl: /avatars/1cfa3a1f59687db58af4e1b4a8767bfd.svg
paper_authors.fullname: Yang
paper_authors.type: user
paper_authors.user: Yiying12
paper_authors._id: 68072d05362af0cf18fb4b13
paper_authors.name: Xianfang Zeng
paper_authors._id: 68072d05362af0cf18fb4b14
paper_authors.name: Gang Yu
paper_authors._id: 68072d05362af0cf18fb4b15
paper_authors.name: Ming Li
paper_title: StyleMe3D: Stylization with Disentangled Priors by Multiple Encoders on
    3D Gaussians
paper_summary: 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.
paper_ai_summary: StyleMe3D enhances 3D Gaussian Splatting for stylized scene reconstruction by integrating multi-modal style conditioning, semantic alignment, and perceptual quality enhancement.
paper_ai_keywords: 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