FabricGen: Microstructure-Aware Woven Fabric Generation

Yingjie Tang; Di Luo; Zixiong Wang; Xiaoli Ling; jian Yang; Beibei Wang

cs.CVMar 7, 2026

FabricGen: Microstructure-Aware Woven Fabric Generation

Authors: Yingjie Tang, Di Luo, Zixiong Wang, Xiaoli Ling, jian Yang, Beibei Wang

Organizations: College of Computer Science, Nankai University, Tianjin, China · School of Intelligence Science and Technology, Nanjing University, Suzhou, China

Abstract

Woven fabric materials are widely used in rendering applications, yet designing realistic examples typically involves multiple stages, requiring expertise in weaving principles and texture authoring. Recent advances have explored diffusion models to streamline this process; however, pre-trained diffusion models often struggle to generate intricate yarn-level details that conform to weaving rules. To address this, we present FabricGen, an end-to-end framework for generating high-quality woven fabric materials from textual descriptions. A key insight of our method is the decomposition of macro-scale textures and micro-scale weaving patterns. To generate macro-scale textures free from microstructures, we fine-tune pre-trained diffusion models on a collected dataset of microstructure-free fabrics. As for micro-scale weaving patterns, we develop an enhanced procedural geometric model capable of synthesizing natural yarn-level geometry with yarn sliding and flyaway fibers. The procedural model is driven by a specialized large language model, WeavingLLM, which is fine-tuned on an annotated dataset of formatted weaving drafts, and prompt-tuned with domain-specific fabric expertise. Through fine-tuning and prompt tuning, WeavingLLM learns to design weaving drafts and fabric parameters from textual prompts, enabling the procedural model to produce diverse weaving patterns that stick to weaving principles. The generated macro-scale texture, along with the micro-scale geometry, can be used for fabric rendering. Consequently, our framework produces materials with significantly richer detail and realism compared to prior generative models.

Figures & tables

Figure 2: Overview of FabricGen. Our fabric material creation framework consists of two main components: (a) an LLM-driven procedural microstructure generator that generates yarn-level microstructures along with fabric parameters, and (b) a texture generator that generates tileable, macro-scale textures from a text prompt (and optional image). Note that the two components require distinct prompts.

Figure 3: Woven fabrics are typically constructed by interlacing weft and warp yarns following specific patterns (b), which can be represented via a binary matrix, named weaving draft (a). Each yarn may consist of multiple plies, and each ply can be further divided into a number of fibers. Some fibers escape from the surface, resulting in irregular flyaway fibers (c).

Figure 4: Curved helix model for multi-ply yarns. The model achieves analytical normal/orientation formulation, where the yarn space arc parameters

u,v

are linear mapped from UV space.

Figure 5: Visualization of the normal, orientation and height maps of a 3-ply basket weave. The parameters of this case are provided in the supplementary material.

Figure 6: Comparison on text conditioning. Given text prompt, DressCode [ 4 ] and MatFuse [ 22 ] generate PBR textures, which struggle to capture fine fabric details, resulting in blurring and distortion in close-up views. In contrast, our method generates procedural microstructure together with macroscale texture, ensuring a realistic appearance in both macro and close-up views.

	MatFuse	DressCode	Ours
	Text conditioning
CLIP Score $\uparrow$	0.240	0.307	0.317
User study $\uparrow$	1.43%	16.02%	82.55%
	Text and image conditioning
CLIP-I Score $\uparrow$	0.722	N/A	0.827

Table 1: Quantitative comparisons between our method, MatFuse [ 22 ] , and DressCode [ 4 ] .

Figure 7: Comparison on text and image conditioning . Given both text and image prompts, our generated materials achieving better consistency and richer detail than MatFuse [ 17 ] .

Figure 8: Comparison among WeavingLLM, Qwen2.5 [ 20 ] , and GPT-5 [ 13 ] . COSSIM indicates the cosine similarity of Fourier spectra, and PERF indicates the user preferences ratio. All renderings use a pure gray albedo map. More reference drafts are provided in the supplementary material.

Figure 9: Impact of prompt-tuning . The parameters predicted by WeavingLLM successfully reproduce the rough appearance of herringbone twill and the anisotropic sheen of satin, whereas parameters predicted by the base LLM fail to reproduce this effect.

Figure 10: Impact of macro-scale texture generator . The diffusion model pre-trained on natural images occasionally produces unintended 3D artifacts (first row) or implicit stripes (third row), whereas our fine-tuned model is capable of generating microstructure-free albedo maps. The results in the fourth column use the regular twill pattern in Fig 2 .

Figure 11: Impact of irregular effects . Irregular yarn displacements in real fabrics often result in strip-like gaps between yarns, and fibers escaping from the surface cause irregular highlights.

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Automated generation of production-ready 3D garment assets from a single image is a central challenge in digital content creation. While recent generative models have significantly advanced 3D geometry reconstruction, synthesizing high-quality textures remains a bottleneck. Existing methods often bake environmental illumination and shadows directly into the texture map, or they fail to maintain global structural coherence, making the resulting assets unusable for physical simulation and relighting. In this work, we introduce OmniFabric, a novel approach that synthesizes globally coherent texture maps directly within the 2D sewing pattern space. Given a single reference image, our pipeline utilizes an estimated 3D mesh and generative priors of powerful Vision-Language Models (VLM) to establish a complete but coarse texture initialization across the unwrapped sewing patterns. We then leverage a specialized diffusion transformer, trained via an automated synthetic data engine and conditioned on 3D positional features, to refine this initialization directly in the canonical UV domain. This effectively removes distortion and baked-in artifacts to extract a clean and normalized texture map that preserves the original garment design. Extensive experiments demonstrate that OmniFabric significantly outperforms state-of-the-art baselines, yielding photorealistic 3D garments with high-quality textures.

Abstract

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