# Fast Fourier Asymmetric Context Aggregation Network:
## 1 Introduction
Additionally, creating detailed texture references remains a laborious and time-intensive process, posing additional challenges for designers. Although some studies on texture synthesis [15; 16; 17] have achieved impressive results using limited texture content, the outcomes are often restricted to simple, repetitive patterns and suffer from issues such as edge-seam artifacts. Moreover, these methods struggle to accurately capture critical features of clothing images, such as lighting effects and fabric folds. Consequently, utilizing such techniques as a source for clothing style remains remains fraught with difficulties. The above issues highlight the need for advanced technologies that can seamlessly integrate the structural guidance of design sketches with precise texture control to generate realistic garment images. Such methods would allow designers to quickly obtain high-quality images that align with their creative intentions, significantly enhancing the practical value of these techniques. To achieve this goal, we propose a novel fashion clothing image generation network (FCAN) that combines Fast Fourier Convolution (FFC) [18] with a novel Asymmetric Context Aggregation Mechanism (ACAM) to generate clothing images that align with the intended design structures and textures. By utilizing dual guidance from design sketches and texture patches, the method ensures that the structural semantic information from the sketches is preserved while enabling precise control over texture styles. Within the FCAN, we leverage the spectral transformation capability of FFC to design a Fast Fourier Channel Dual Residual Block (FF-CDRB), which efficiently handles complex textures and detailed information for content inference. Additionally, we introduce the ACAM in the FCAN, which innovatively aggregates global context information through multi-stage heterogeneous features. This mechanism partially compensates for the inadequate context modeling capabilities of the existing methods, enabling accurate integration of sketch information with limited texture content. Extensive comparative experiments demonstrate that this method exhibits superior robustness to variations in texture patch size compared to the existing advanced methods, and it is capable of synthesizing high-quality clothing images across diverse texture conditions. In addition, extensive ablation studies demonstrate that, within the FCAN, the ACAM effectively addresses the limitations of the existing methods in long-range context modeling, significantly improving the quality of synthesized images and enhancing the model's robustness to variations in texture patch size. The integration of the FF-CDRB further boosts the model's ability to capture complex textures and fine details while optimizing parameter efficiency. Together, these innovations establish the FCAN as a practical and effective solution, capable of making a positive impact in real-world applications, particularly in enhancing the efficiency of fashion design processes. Figure 1 demonstrates the clothing image synthesis results produced by our proposed method. Notably, by guiding the generation process with texture patches and design sketches that include brief internal contours, our method is able to flexibly synthesize complex structures and diverse texture content. Furthermore, it exhibits positive effects in areas such as wrinkles and shadows. It is worth noting that the texture patches in the images are derived from fabric image cropping, Photoshop compositing, or real garment cutting. Regardless of the source, our method consistently demonstrates strong generative performance, highlighting its flexibility and reliability. In summary, our main contributions are as follows:  
* We propose a novel clothing image synthesis method guided by design sketches and texture patches, enabling precise control over both the structural appearance and texture style of the clothing. Our method provides innovative and practical solutions for scenarios such as personalized clothing design and rapid prototyping.