AnyStyle: Single-Pass Multimodal Stylization for 3D Gaussian Splatting

• URL: http://arxiv.org/abs/2602.04043v1
• Date: Tue, 03 Feb 2026 22:19:58 GMT
• Title: AnyStyle: Single-Pass Multimodal Stylization for 3D Gaussian Splatting
• Authors: Joanna Kaleta, Bartosz Świrta, Kacper Kania, Przemysław Spurek, Marek Kowalski,
• Abstract summary: We introduce AnyStyle, a feed-forward 3D reconstruction and stylization framework that enables pose-free, zero-shot stylization through multimodal conditioning.<n>Our method supports both textual and visual style inputs, allowing users to control the scene appearance using natural language descriptions or reference images.<n>Experiments demonstrate that AnyStyle improves style controllability over prior feed-forward stylization methods while preserving high-quality geometric reconstruction.
• Score: 3.8078651836376007
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The growing demand for rapid and scalable 3D asset creation has driven interest in feed-forward 3D reconstruction methods, with 3D Gaussian Splatting (3DGS) emerging as an effective scene representation. While recent approaches have demonstrated pose-free reconstruction from unposed image collections, integrating stylization or appearance control into such pipelines remains underexplored. Existing attempts largely rely on image-based conditioning, which limits both controllability and flexibility. In this work, we introduce AnyStyle, a feed-forward 3D reconstruction and stylization framework that enables pose-free, zero-shot stylization through multimodal conditioning. Our method supports both textual and visual style inputs, allowing users to control the scene appearance using natural language descriptions or reference images. We propose a modular stylization architecture that requires only minimal architectural modifications and can be integrated into existing feed-forward 3D reconstruction backbones. Experiments demonstrate that AnyStyle improves style controllability over prior feed-forward stylization methods while preserving high-quality geometric reconstruction. A user study further confirms that AnyStyle achieves superior stylization quality compared to an existing state-of-the-art approach. Repository: https://github.com/joaxkal/AnyStyle.

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