用最优传输控制风格分配,让3D高斯点云多视角风格更一致。
Capacity-Controlled Multi-View Stylization of 3D Gaussian Splatting

- 基于半平衡最优传输重构风格匹配,控制特征分配容量。
- 跨视图匹配引导提升风格一致性,避免特征重复使用。
- 适合需要稳定3D风格化效果的视觉生成研究者。
尽管3D高斯点云(3DGS)为新视角合成提供了高效显式表示,但实现多视角间风格一致性仍具挑战。现有方法通常对每个渲染视图独立应用2D特征匹配损失,导致风格分配不稳定、多对一特征复用及跨视图一致性差。本文提出一种基于最优传输的容量可控多视图风格化框架。将局部风格匹配重新建模为半平衡最优传输问题,通过引入可调节强度的列容量约束,缓解多对一匹配现象,实现风格特征的可控分配。该传输目标在保持跨视图稳定对应的同时,平衡特征覆盖率与风格多样性。为进一步增强跨视图一致性,引入新型跨视图匹配引导,约束场景内容与风格模式间的对应关系。此外,设计多种几何正则化项优化原始3DGS,使高斯原语在风格化过程中能表示更精细纹理。大量实验表明,本方法显著提升多视角风格一致性,生成稳定且富有表现力的3D风格化结果,同时保留场景核心语义结构。
原文摘要 · Abstract (English)
While 3D Gaussian Splatting (3DGS) provides an efficient and explicit representation for novel view synthesis, enforcing stylistic coherence across viewpoints remains challenging. Existing 3D stylization methods typically apply 2D feature-matching losses independently per rendered view, which leads to unstable style allocation, many-to-one feature reuse, and limited cross-view consistency. We propose a capacity-controlled framework for multi-view stylization of 3DGS, grounded in optimal transport. Specifically, we reformulate local style matching as a semi-balanced optimal transport problem. By introducing explicit column-capacity constraints with tunable strength, our formulation mitigates many-to-one matching and enables controllable allocation of style features. This transport-based objective provides a principled mechanism for balancing feature coverage and stylistic diversity while maintaining stable correspondences across viewpoints. To further enhance cross-view coherence, we incorporate a novel cross-view matching guidance to constrain correspondences between scene content and style patterns. In addition, we introduce several geometric regularizations to enhance the vanilla 3DGS, thereby enabling optimized Gaussian primitives to represent finer-grained textures during stylization. Extensive experiments demonstrate that our approach significantly improves multi-view stylistic consistency and produces stable, expressive 3D stylizations while preserving the core semantic structure of the scene.
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