Styl3R: Instant 3D Stylized Reconstruction for Arbitrary Scenes and
  Styles
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.
683818841902f641cc669774
Peng Wang
683818841902f641cc669775
Xiang Liu
683818841902f641cc669776
Peidong Liu
Styl3R: Instant 3D Stylized Reconstruction for Arbitrary Scenes and
  Styles
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.
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.
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