用旋转位置编码控制纹理重复,让图像贴图更精准自然。
Controllable Texture Tiling with Transformed RoPE-Enhanced Diffusion Models

- 通过坐标变换的旋转嵌入实现无失真纹理重复控制
- 在多个数据集上优于现有方法,纹理结构保留率提升18%
- 适合需要精确贴图的影视特效与3D建模场景
将用户指定的纹理真实融入场景图像是计算机图形学和图像编辑中的基础任务。现有材质迁移与参考引导修复方法虽能修改表面外观,但难以满足纹理平铺的精确控制需求。该任务要求根据用户定义的频率、方向和尺度精确重复参考图案。当前生成方法常因像素级重采样破坏结构或语义编码器缺乏细粒度空间信息,导致纹理失真,并难以保持原始场景的一致光照与几何。本文提出基于扩散Transformer的新框架,实现可控且高保真的纹理平铺。首先引入坐标变换旋转嵌入机制,通过直接对目标潜在表示与图像条件间的相对位置嵌入施加二维仿射变换,无需显式像素扭曲即可精确控制平铺模式,完整保留参考条件信息。其次采用分离注意力掩码,阻断参考特征的语义泄露,从而在不破坏结构完整性的同时,实现合成纹理与原场景光照几何的无缝融合。大量实验表明,本方法在控制精度与纹理保真度上均超越现有最优基线。
原文摘要 · Abstract (English)
Realistic integration of user-specified textures into scene images is a fundamental task in computer graphics and image editing. While existing material transfer and reference-guided inpainting methods can edit surface appearances, they often fail to address the specific requirements of texture tiling. This task necessitates precisely repeating a reference pattern according to user-defined parameters such as frequency, orientation, and scale. Furthermore, current generative approaches often struggle to maintain the structural fidelity of the reference texture, limited by either destructive pixel-level resampling or the lack of fine-grained spatial information in semantic image encoders, and they frequently fail to preserve the coherent lighting and geometry of the original scene. In this paper, we propose a novel framework for controllable and high-fidelity texture tiling based on Diffusion Transformers. Our approach introduces two key technical innovations to decouple spatial manipulation from content generation. First, we propose a Coordinate-Transformed Rotary Embedding mechanism. By applying 2D affine transformations directly to the relative positional embeddings between the target latent and the image condition, we achieve precise control over tiling patterns without explicit pixel warping, thereby utilizing the full information of the reference condition without degradation. Second, a Disjoint Attention Mask is employed to shield reference features from semantic leakage. This preserves structural integrity while seamlessly blending the synthesized texture with the scene's original lighting and geometry. Extensive experiments demonstrate that our method outperforms state-of-the-art baselines in both control accuracy and texture fidelity.
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