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arXiv · 2510.10497

Jigsaw3D: Disentangled 3D Style Transfer via Patch Shuffling and Masking

Yuteng Ye·Zheng Zhang·Qinchuan Zhang·Di Wang·Youjia Zhang·Wenxiao Zhang·Wei Yang·Yuan Liu
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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: https://babahui.github.io/jigsaw3D.github.io/

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Yuteng Ye, Zheng Zhang, Qinchuan Zhang, Di Wang, Youjia Zhang, Wenxiao Zhang, Wei Yang, Yuan Liu. 2025-10-12. Jigsaw3D: Disentangled 3D Style Transfer via Patch Shuffling and Masking. https://arxiv.org/abs/2510.10497

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