Controllable Texture Tiling with Transformed RoPE-Enhanced Diffusion Models

• URL: http://arxiv.org/abs/2606.22945v1
• Date: Mon, 22 Jun 2026 07:24:15 GMT
• Title: Controllable Texture Tiling with Transformed RoPE-Enhanced Diffusion Models
• Abstract summary: We propose a novel framework for controllable and high-fidelity texture tiling based on Diffusion Transformers.<n>Our approach introduces two key technical innovations to decouple spatial manipulation from content generation.<n>Our method outperforms state-of-the-art baselines in both control accuracy and texture fidelity.
• Score: 21.26349994452928
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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