Styl3R: Instant 3D Stylized Reconstruction for Arbitrary Scenes and Styles

• URL: http://arxiv.org/abs/2505.21060v1
• Date: Tue, 27 May 2025 11:47:15 GMT
• Title: Styl3R: Instant 3D Stylized Reconstruction for Arbitrary Scenes and Styles
• Authors: Peng Wang, Xiang Liu, Peidong Liu,
• Abstract summary: Current state-of-the-art 3D stylization methods typically involve computationally intensive test-time optimization to transfer artistic features into a pretrained representation.<n>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.
• Score: 10.472018360278085
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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