title: Styl3R: Instant 3D Stylized Reconstruction for Arbitrary Scenes and
    Styles
summary: Stylizing 3D scenes instantly while maintaining multi-view consistency and
  faithfully resembling a style image remains a significant challenge. Current
  state-of-the-art 3D stylization methods typically involve computationally
  intensive test-time optimization to transfer artistic features into a
  pretrained 3D representation, often requiring dense posed input images. In
  contrast, leveraging recent advances in feed-forward reconstruction models, we
  demonstrate a novel approach to achieve direct 3D stylization in less than a
  second using unposed sparse-view scene images and an arbitrary style image. To
  address the inherent decoupling between reconstruction and stylization, we
  introduce a branched architecture that separates structure modeling and
  appearance shading, effectively preventing stylistic transfer from distorting
  the underlying 3D scene structure. Furthermore, we adapt an identity loss to
  facilitate pre-training our stylization model through the novel view synthesis
  task. This strategy also allows our model to retain its original reconstruction
  capabilities while being fine-tuned for stylization. Comprehensive evaluations,
  using both in-domain and out-of-domain datasets, demonstrate that our approach
  produces high-quality stylized 3D content that achieve a superior blend of
  style and scene appearance, while also outperforming existing methods in terms
  of multi-view consistency and efficiency.
paper_authors._id: 683818841902f641cc669774
paper_authors.name: Peng Wang
paper_authors._id: 683818841902f641cc669775
paper_authors.name: Xiang Liu
paper_authors._id: 683818841902f641cc669776
paper_authors.name: Peidong Liu
paper_title: Styl3R: Instant 3D Stylized Reconstruction for Arbitrary Scenes and
    Styles
paper_summary: Stylizing 3D scenes instantly while maintaining multi-view consistency and
  faithfully resembling a style image remains a significant challenge. Current
  state-of-the-art 3D stylization methods typically involve computationally
  intensive test-time optimization to transfer artistic features into a
  pretrained 3D representation, often requiring dense posed input images. In
  contrast, leveraging recent advances in feed-forward reconstruction models, we
  demonstrate a novel approach to achieve direct 3D stylization in less than a
  second using unposed sparse-view scene images and an arbitrary style image. To
  address the inherent decoupling between reconstruction and stylization, we
  introduce a branched architecture that separates structure modeling and
  appearance shading, effectively preventing stylistic transfer from distorting
  the underlying 3D scene structure. Furthermore, we adapt an identity loss to
  facilitate pre-training our stylization model through the novel view synthesis
  task. This strategy also allows our model to retain its original reconstruction
  capabilities while being fine-tuned for stylization. Comprehensive evaluations,
  using both in-domain and out-of-domain datasets, demonstrate that our approach
  produces high-quality stylized 3D content that achieve a superior blend of
  style and scene appearance, while also outperforming existing methods in terms
  of multi-view consistency and efficiency.
paper_ai_summary: A novel feed-forward model achieves fast 3D stylization using sparse view images, maintaining multi-view consistency and high-quality style transfer while retaining reconstruction accuracy.
paper_ai_keywords: feed-forward reconstruction models 3D stylization dense posed input images unposed sparse-view scene images branched architecture structure modeling appearance shading identity loss novel view synthesis in-domain datasets out-of-domain datasets multi-view consistency