Stylos: Multi-View 3D Stylization with Single-Forward Gaussian Splatting

• URL: http://arxiv.org/abs/2509.26455v1
• Date: Tue, 30 Sep 2025 16:09:13 GMT
• Title: Stylos: Multi-View 3D Stylization with Single-Forward Gaussian Splatting
• Authors: Hanzhou Liu, Jia Huang, Mi Lu, Srikanth Saripalli, Peng Jiang,
• Abstract summary: We present Stylos, a single-forward 3D Gaussian framework for 3D style transfer that operates on unposed content.<n>Stylos synthesizes a stylized 3D scene without per-scene optimization or precomputed poses.<n> Experiments across multiple datasets demonstrate that Stylos delivers high-quality zero-shot stylization.
• Score: 11.720515089961339
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present Stylos, a single-forward 3D Gaussian framework for 3D style transfer that operates on unposed content, from a single image to a multi-view collection, conditioned on a separate reference style image. Stylos synthesizes a stylized 3D Gaussian scene without per-scene optimization or precomputed poses, achieving geometry-aware, view-consistent stylization that generalizes to unseen categories, scenes, and styles. At its core, Stylos adopts a Transformer backbone with two pathways: geometry predictions retain self-attention to preserve geometric fidelity, while style is injected via global cross-attention to enforce visual consistency across views. With the addition of a voxel-based 3D style loss that aligns aggregated scene features to style statistics, Stylos enforces view-consistent stylization while preserving geometry. Experiments across multiple datasets demonstrate that Stylos delivers high-quality zero-shot stylization, highlighting the effectiveness of global style-content coupling, the proposed 3D style loss, and the scalability of our framework from single view to large-scale multi-view settings.

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