Foodfusion: A Novel Approach for Food Image Composition via Diffusion Models
<|reference_start|>Foodfusion: A Novel Approach for Food Image Composition via Diffusion Models: Food image composition requires the use of existing dish images and background images to synthesize a natural new image, while diffusion models have made significant advancements in image generation, enabling the construction of end-to-end architectures that yield promising results. However, existing diffusion models face challenges in processing and fusing information from multiple images and lack access to high-quality publicly available datasets, which prevents the application of diffusion models in food image composition. In this paper, we introduce a large-scale, high-quality food image composite dataset, FC22k, which comprises 22,000 foreground, background, and ground truth ternary image pairs. Additionally, we propose a novel food image composition method, Foodfusion, which leverages the capabilities of the pre-trained diffusion models and incorporates a Fusion Module for processing and integrating foreground and background information. This fused information aligns the foreground features with the background structure by merging the global structural information at the cross-attention layer of the denoising UNet. To further enhance the content and structure of the background, we also integrate a Content-Structure Control Module. Extensive experiments demonstrate the effectiveness and scalability of our proposed method.<|reference_end|>
@article{shi2024foodfusion:,
    title={Foodfusion: A Novel Approach for Food Image Composition via Diffusion
  Models},
    author={Chaohua Shi, Xuan Wang, Si Shi, Xule Wang, Mingrui Zhu, Nannan Wang,
  Xinbo Gao},
    journal={arXiv preprint arXiv:2408.14135},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.14135},
    primaryClass={cs.CV}
}