Abstract
Controllable 3D style transfer seeks to restyle a 3D asset so that its textures match a reference image while preserving the integrity and multi-view consistency. The prevalent methods either rely on direct reference style token injection or score-distillation from 2D diffusion models, which incurs heavy per-scene optimization and often entangles style with semantic content. We introduce Jigsaw3D, a multi-view diffusion based pipeline that decouples style from content and enables fast, view-consistent stylization. Our key idea is to leverage the jigsaw operation - spatial shuffling and random masking of reference patches - to suppress object semantics and isolate stylistic statistics (color palettes, strokes, textures). We integrate these style cues into a multi-view diffusion model via reference-to-view cross-attention, producing view-consistent stylized renderings conditioned on the input mesh. The renders are then style-baked onto the surface to yield seamless textures. Across standard 3D stylization benchmarks, Jigsaw3D achieves high style fidelity and multi-view consistency with substantially lower latency, and generalizes to masked partial reference stylization, multi-object scene styling, and tileable texture generation. Project page is available at: this https URL
Abstract (translated)
可控的3D风格转换旨在重新设计一个3D资产，使其纹理与参考图像相匹配，并同时保持其完整性和多视角一致性。目前流行的方法要么依赖于直接引用样式令牌注入或从2D扩散模型进行评分蒸馏，这需要大量的场景级优化，且经常将风格与语义内容纠缠在一起。 我们引入了Jigsaw3D，这是一种基于多视图扩散的流水线，它可以将风格和内容分离，并实现快速、视角一致性的风格化。我们的核心思想是利用拼图操作（参考补丁的空间打乱和随机掩码）来抑制物体语义并隔离样式统计信息（调色板、笔触、纹理）。我们通过视图交叉注意力机制将这些样式提示集成到多视图扩散模型中，生成以输入网格为条件的视角一致化的风格化渲染。然后将这些渲染中的样式烘焙到表面，以产生无缝的纹理。 在标准的3D风格化基准测试中，Jigsaw3D实现了高风格保真度和多视角一致性，并且延迟明显降低。此外，该方法还能推广至部分遮挡参考样式的风格化、多个物体场景的风格化以及可平铺纹理生成等应用场景。项目页面可在以下链接访问：[此URL](this%20https%20URL)（请将占位符替换为实际链接）。
URL
https://arxiv.org/abs/2510.10497
